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Dual Debiasing for Noisy In-Context Learning for Text Generation | 2506.00418v1 | min2022rethinking | \cite{min2022rethinking} | Rethinking the Role of Demonstrations: What Makes In-Context Learning
Work? | http://arxiv.org/abs/2202.12837v2 | Large language models (LMs) are able to in-context learn -- perform a new
task via inference alone by conditioning on a few input-label pairs
(demonstrations) and making predictions for new inputs. However, there has been
little understanding of how the model learns and which aspects of the
demonstrations contribute to... | true | true | Min, Sewon and Lyu, Xinxi and Holtzman, Ari and Artetxe, Mikel and Lewis, Mike and Hajishirzi, Hannaneh and Zettlemoyer, Luke | 2,022 | null | null | null | arXiv preprint arXiv:2202.12837 | Rethinking the Role of Demonstrations: What Makes In-Context Learning
Work? | [PDF] What Makes In-Context Learning Work? - ACL Anthology | https://aclanthology.org/2022.emnlp-main.759.pdf | Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? Large language models (LMs) are able to in- context learn—perform a new task via |
Dual Debiasing for Noisy In-Context Learning for Text Generation | 2506.00418v1 | kang2024context | \cite{kang2024context} | In-Context Learning with Noisy Labels | http://arxiv.org/abs/2411.19581v1 | In-context learning refers to the emerging ability of large language models
(LLMs) to perform a target task without additional training, utilizing
demonstrations of the task. Recent studies aim to enhance in-context learning
performance by selecting more useful demonstrations. However, they overlook the
presence of ine... | true | true | Kang, Junyong and Son, Donghyun and Song, Hwanjun and Chang, Buru | 2,024 | null | null | null | arXiv preprint arXiv:2411.19581 | In-Context Learning with Noisy Labels | [2411.19581] In-Context Learning with Noisy Labels - arXiv | https://arxiv.org/abs/2411.19581 | In this paper, we propose a new task, in-context learning with noisy labels, which aims to solve real-world problems for in-context learning. |
Dual Debiasing for Noisy In-Context Learning for Text Generation | 2506.00418v1 | gao2024noise | \cite{gao2024noise} | On the Noise Robustness of In-Context Learning for Text Generation | http://arxiv.org/abs/2405.17264v3 | Large language models (LLMs) have shown impressive performance on downstream
tasks by in-context learning (ICL), which heavily relies on the quality of
demonstrations selected from a large set of annotated examples. Recent works
claim that in-context learning is robust to noisy demonstrations in text
classification. In... | true | true | Gao, Hongfu and Zhang, Feipeng and Jiang, Wenyu and Shu, Jun and Zheng, Feng and Wei, Hongxin | 2,024 | null | null | null | null | On the Noise Robustness of In-Context Learning for Text Generation | On the Noise Robustness of In-Context Learning for Text ... | https://openreview.net/forum?id=00uVk06eVK&referrer=%5Bthe%20profile%20of%20Hongxin%20Wei%5D(%2Fprofile%3Fid%3D~Hongxin_Wei1) | The paper "On the Noise Robustness of In-Context Learning for Text Generation" investigates how LLMs handle noisy annotations during in-context |
Dual Debiasing for Noisy In-Context Learning for Text Generation | 2506.00418v1 | li2022contrastive | \cite{li2022contrastive} | Contrastive Decoding: Open-ended Text Generation as Optimization | http://arxiv.org/abs/2210.15097v2 | Given a language model (LM), maximum probability is a poor decoding objective
for open-ended generation, because it produces short and repetitive text. On
the other hand, sampling can often produce incoherent text that drifts from the
original topics. We propose contrastive decoding (CD), a reliable decoding
approach t... | true | true | Li, Xiang Lisa and Holtzman, Ari and Fried, Daniel and Liang, Percy and Eisner, Jason and Hashimoto, Tatsunori and Zettlemoyer, Luke and Lewis, Mike | 2,022 | null | null | null | arXiv preprint arXiv:2210.15097 | Contrastive Decoding: Open-ended Text Generation as Optimization | Contrastive Decoding: Open-ended Text Generation as Optimization | https://arxiv.org/abs/2210.15097 | We propose contrastive decoding (CD), a reliable decoding approach that optimizes a contrastive objective subject to a plausibility constraint. |
Dual Debiasing for Noisy In-Context Learning for Text Generation | 2506.00418v1 | zhao2024enhancing | \cite{zhao2024enhancing} | Enhancing Contextual Understanding in Large Language Models through
Contrastive Decoding | http://arxiv.org/abs/2405.02750v1 | Large language models (LLMs) tend to inadequately integrate input context
during text generation, relying excessively on encoded prior knowledge in model
parameters, potentially resulting in generated text with factual
inconsistencies or contextually unfaithful content. LLMs utilize two primary
knowledge sources: 1) pr... | true | true | Zhao, Zheng and Monti, Emilio and Lehmann, Jens and Assem, Haytham | 2,024 | null | null | null | arXiv preprint arXiv:2405.02750 | Enhancing Contextual Understanding in Large Language Models through
Contrastive Decoding | Enhancing Contextual Understanding in Large Language Models ... | https://aclanthology.org/2024.naacl-long.237/ | We introduce a novel approach integrating contrastive decoding with adversarial irrelevant passages as negative samples to enhance robust context grounding |
Dual Debiasing for Noisy In-Context Learning for Text Generation | 2506.00418v1 | fei2023mitigating | \cite{fei2023mitigating} | Mitigating Label Biases for In-context Learning | http://arxiv.org/abs/2305.19148v3 | Various design settings for in-context learning (ICL), such as the choice and
order of the in-context examples, can bias a model toward a particular
prediction without being reflective of an understanding of the task. While many
studies discuss these design choices, there have been few systematic
investigations into ca... | true | true | Fei, Yu and Hou, Yifan and Chen, Zeming and Bosselut, Antoine | 2,023 | null | null | null | arXiv preprint arXiv:2305.19148 | Mitigating Label Biases for In-context Learning | [2305.19148] Mitigating Label Biases for In-context Learning - arXiv | https://arxiv.org/abs/2305.19148 | In this work, we define a typology for three types of label biases in ICL for text classification: vanilla-label bias, context-label bias, and domain-label |
Dual Debiasing for Noisy In-Context Learning for Text Generation | 2506.00418v1 | zhao2021calibrate | \cite{zhao2021calibrate} | Calibrate Before Use: Improving Few-Shot Performance of Language Models | http://arxiv.org/abs/2102.09690v2 | GPT-3 can perform numerous tasks when provided a natural language prompt that
contains a few training examples. We show that this type of few-shot learning
can be unstable: the choice of prompt format, training examples, and even the
order of the training examples can cause accuracy to vary from near chance to
near sta... | true | true | Zhao, Zihao and Wallace, Eric and Feng, Shi and Klein, Dan and Singh, Sameer | 2,021 | null | null | null | null | Calibrate Before Use: Improving Few-Shot Performance of Language Models | Calibrate Before Use: Improving Few-Shot Performance of ... | http://proceedings.mlr.press/v139/zhao21c/zhao21c.pdf | by Z Zhao · 2021 · Cited by 1608 — Overall, contextual calibration is a simple method that makes language models better few-shot learners: it enables end users to obtain higher accuracy with. |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | NIPS2013_9aa42b31 | \cite{NIPS2013_9aa42b31} | Distributed Representations of Words and Phrases and their
Compositionality | http://arxiv.org/abs/1310.4546v1 | The recently introduced continuous Skip-gram model is an efficient method for
learning high-quality distributed vector representations that capture a large
number of precise syntactic and semantic word relationships. In this paper we
present several extensions that improve both the quality of the vectors and the
traini... | true | true | Tom{\'{a}}s Mikolov and
Ilya Sutskever and
Kai Chen and
Gregory S. Corrado and
Jeffrey Dean | 2,013 | null | https://proceedings.neurips.cc/paper/2013/hash/9aa42b31882ec039965f3c4923ce901b-Abstract.html | null | null | Distributed Representations of Words and Phrases and their
Compositionality | [PDF] Distributed Representations of Words and Phrases and their ... | https://proceedings.neurips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf | Distributed representations of words use vector spaces to group similar words, capturing syntactic and semantic relationships, and are limited by their |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | pennington-etal-2014-glove | \cite{pennington-etal-2014-glove} | Glove: Global Vectors for Word Representation | null | null | true | false | Jeffrey Pennington and
Richard Socher and
Christopher D. Manning | 2,014 | null | https://doi.org/10.3115/v1/d14-1162 | 10.3115/V1/D14-1162 | null | Glove: Global Vectors for Word Representation | GloVe: Global Vectors for Word Representation | https://nlp.stanford.edu/projects/glove/ | GloVe: Global Vectors for Word Representation GloVe: Global Vectors for Word RepresentationJeffrey Pennington, Richard Socher, Christopher D. GloVe: Global Vectors for Word Representation. GloVe is designed in order that such vector differences capture as much as possible the meaning specified by the juxtaposition of t... |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | transformer | \cite{transformer} | Attention Is All You Need | http://arxiv.org/abs/1706.03762v7 | The dominant sequence transduction models are based on complex recurrent or
convolutional neural networks in an encoder-decoder configuration. The best
performing models also connect the encoder and decoder through an attention
mechanism. We propose a new simple network architecture, the Transformer, based
solely on at... | true | true | Ashish Vaswani and
Noam Shazeer and
Niki Parmar and
Jakob Uszkoreit and
Llion Jones and
Aidan N. Gomez and
Lukasz Kaiser and
Illia Polosukhin | 2,017 | null | https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html | null | null | Attention Is All You Need | Attention Is All You Need | http://arxiv.org/pdf/1706.03762v7 | The dominant sequence transduction models are based on complex recurrent or
convolutional neural networks in an encoder-decoder configuration. The best
performing models also connect the encoder and decoder through an attention
mechanism. We propose a new simple network architecture, the Transformer, based
solely on at... |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | devlin-etal-2019-bert | \cite{devlin-etal-2019-bert} | BERT: Pre-training of Deep Bidirectional Transformers for Language
Understanding | http://arxiv.org/abs/1810.04805v2 | We introduce a new language representation model called BERT, which stands
for Bidirectional Encoder Representations from Transformers. Unlike recent
language representation models, BERT is designed to pre-train deep
bidirectional representations from unlabeled text by jointly conditioning on
both left and right contex... | true | true | Jacob Devlin and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova | 2,019 | null | https://doi.org/10.18653/v1/n19-1423 | 10.18653/V1/N19-1423 | null | BERT: Pre-training of Deep Bidirectional Transformers for Language
Understanding | [PDF] BERT: Pre-training of Deep Bidirectional Transformers for Language ... | https://aclanthology.org/N19-1423.pdf | Unlike recent language repre-sentation models (Peters et al., 2018a; Rad-ford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a re-sult, the pre-trained BERT model can be fine-tuned with just one ... |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | cer-etal-2018-universal | \cite{cer-etal-2018-universal} | Universal Sentence Encoder for English | null | null | true | false | Daniel Cer and
Yinfei Yang and
Sheng{-}yi Kong and
Nan Hua and
Nicole Limtiaco and
Rhomni St. John and
Noah Constant and
Mario Guajardo{-}Cespedes and
Steve Yuan and
... | 2,018 | null | https://doi.org/10.18653/v1/d18-2029 | 10.18653/V1/D18-2029 | null | Universal Sentence Encoder for English | [1803.11175] Universal Sentence Encoder - arXiv | https://arxiv.org/abs/1803.11175 | We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | reimers-gurevych-2019-sentence | \cite{reimers-gurevych-2019-sentence} | Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks | http://arxiv.org/abs/1908.10084v1 | BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new
state-of-the-art performance on sentence-pair regression tasks like semantic
textual similarity (STS). However, it requires that both sentences are fed into
the network, which causes a massive computational overhead: Finding the most
similar pair i... | true | true | Nils Reimers and
Iryna Gurevych | 2,019 | null | https://doi.org/10.18653/v1/D19-1410 | 10.18653/V1/D19-1410 | null | Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks | [PDF] Sentence Embeddings using Siamese BERT-Networks | https://aclanthology.org/D19-1410.pdf | c ⃝2019 Association for Computational Linguistics 3982 Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks Nils Reimers and Iryna Gurevych Ubiquitous Knowledge Processing Lab (UKP-TUDA) Department of Computer Science, Technische Universit¨ at Darmstadt www.ukp.tu-darmstadt.de Abstract BERT (Devlin et al., 20... |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | gao-etal-2021-simcse | \cite{gao-etal-2021-simcse} | SimCSE: Simple Contrastive Learning of Sentence Embeddings | http://arxiv.org/abs/2104.08821v4 | This paper presents SimCSE, a simple contrastive learning framework that
greatly advances state-of-the-art sentence embeddings. We first describe an
unsupervised approach, which takes an input sentence and predicts itself in a
contrastive objective, with only standard dropout used as noise. This simple
method works sur... | true | true | Tianyu Gao and
Xingcheng Yao and
Danqi Chen | 2,021 | null | https://doi.org/10.18653/v1/2021.emnlp-main.552 | null | null | SimCSE: Simple Contrastive Learning of Sentence Embeddings | SimCSE: Simple Contrastive Learning of Sentence Embeddings | http://arxiv.org/pdf/2104.08821v4 | This paper presents SimCSE, a simple contrastive learning framework that
greatly advances state-of-the-art sentence embeddings. We first describe an
unsupervised approach, which takes an input sentence and predicts itself in a
contrastive objective, with only standard dropout used as noise. This simple
method works sur... |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | zhuo-etal-2023-whitenedcse | \cite{zhuo-etal-2023-whitenedcse} | WhitenedCSE: Whitening-based Contrastive Learning of Sentence Embeddings | null | null | true | false | Wenjie Zhuo and
Yifan Sun and
Xiaohan Wang and
Linchao Zhu and
Yi Yang | 2,023 | null | https://doi.org/10.18653/v1/2023.acl-long.677 | 10.18653/V1/2023.ACL-LONG.677 | null | WhitenedCSE: Whitening-based Contrastive Learning of Sentence Embeddings | Whitening-based Contrastive Learning of Sentence Embeddings | https://aclanthology.org/2023.acl-long.677/ | This paper presents a whitening-based contrastive learning method for sentence embedding learning (WhitenedCSE), which combines contrastive learning with a |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | wang2023improving | \cite{wang2023improving} | Improving Text Embeddings with Large Language Models | http://arxiv.org/abs/2401.00368v3 | In this paper, we introduce a novel and simple method for obtaining
high-quality text embeddings using only synthetic data and less than 1k
training steps. Unlike existing methods that often depend on multi-stage
intermediate pre-training with billions of weakly-supervised text pairs,
followed by fine-tuning with a few... | true | true | Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu | 2,023 | null | https://doi.org/10.48550/arXiv.2401.00368 | null | arXiv | Improving Text Embeddings with Large Language Models | Improving Text Embeddings with Large Language Models | http://arxiv.org/pdf/2401.00368v3 | In this paper, we introduce a novel and simple method for obtaining
high-quality text embeddings using only synthetic data and less than 1k
training steps. Unlike existing methods that often depend on multi-stage
intermediate pre-training with billions of weakly-supervised text pairs,
followed by fine-tuning with a few... |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | muennighoff2024generative | \cite{muennighoff2024generative} | Generative Representational Instruction Tuning | http://arxiv.org/abs/2402.09906v3 | All text-based language problems can be reduced to either generation or
embedding. Current models only perform well at one or the other. We introduce
generative representational instruction tuning (GRIT) whereby a large language
model is trained to handle both generative and embedding tasks by
distinguishing between th... | true | true | Niklas Muennighoff and
Hongjin Su and
Liang Wang and
Nan Yang and
Furu Wei and
Tao Yu and
Amanpreet Singh and
Douwe Kiela | 2,025 | null | https://openreview.net/forum?id=BC4lIvfSzv | null | null | Generative Representational Instruction Tuning | Generative Representational Instruction Tuning | http://arxiv.org/pdf/2402.09906v3 | All text-based language problems can be reduced to either generation or
embedding. Current models only perform well at one or the other. We introduce
generative representational instruction tuning (GRIT) whereby a large language
model is trained to handle both generative and embedding tasks by
distinguishing between th... |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | lei-etal-2024-meta | \cite{lei-etal-2024-meta} | Meta-Task Prompting Elicits Embeddings from Large Language Models | http://arxiv.org/abs/2402.18458v2 | We introduce a new unsupervised text embedding method, Meta-Task Prompting
with Explicit One-Word Limitation (MetaEOL), for generating high-quality
sentence embeddings from Large Language Models (LLMs) without the need for
model fine-tuning. Leveraging meta-task prompting, MetaEOL guides LLMs to
produce embeddings thro... | true | true | Yibin Lei and
Di Wu and
Tianyi Zhou and
Tao Shen and
Yu Cao and
Chongyang Tao and
Andrew Yates | 2,024 | null | https://doi.org/10.18653/v1/2024.acl-long.546 | 10.18653/V1/2024.ACL-LONG.546 | null | Meta-Task Prompting Elicits Embeddings from Large Language Models | [PDF] Meta-Task Prompting Elicits Embeddings from Large Language ... | https://aclanthology.org/2024.acl-long.546.pdf | Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10141–10157 August 11-16, 2024 ©2024 Association for Computational Linguistics Meta-Task Prompting Elicits Embeddings from Large Language Models Yibin Lei1*, Di Wu1, Tianyi Zhou2, Tao Shen3, Yu Cao4, C... |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | li-li-2024-aoe | \cite{li-li-2024-aoe} | AoE: Angle-optimized Embeddings for Semantic Textual Similarity | null | null | true | false | Xianming Li and
Jing Li | 2,024 | null | https://doi.org/10.18653/v1/2024.acl-long.101 | 10.18653/V1/2024.ACL-LONG.101 | null | AoE: Angle-optimized Embeddings for Semantic Textual Similarity | AoE: Angle-optimized Embeddings for Semantic Textual Similarity | https://aclanthology.org/2024.acl-long.101/ | We propose a novel Angle-optimized Embedding model, AoE. It optimizes angle differences in complex space to explore similarity in saturation zones better. |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | su-etal-2023-one | \cite{su-etal-2023-one} | One Embedder, Any Task: Instruction-Finetuned Text Embeddings | http://arxiv.org/abs/2212.09741v3 | We introduce INSTRUCTOR, a new method for computing text embeddings given
task instructions: every text input is embedded together with instructions
explaining the use case (e.g., task and domain descriptions). Unlike encoders
from prior work that are more specialized, INSTRUCTOR is a single embedder that
can generate ... | true | true | Su, Hongjin and
Shi, Weijia and
Kasai, Jungo and
Wang, Yizhong and
Hu, Yushi and
Ostendorf, Mari and
Yih, Wen-tau and
Smith, Noah A. and
Zettlemoyer, Luke and
Yu, Tao | 2,023 | null | https://aclanthology.org/2023.findings-acl.71/ | null | null | One Embedder, Any Task: Instruction-Finetuned Text Embeddings | One Embedder, Any Task: Instruction-Finetuned Text Embeddings | https://aclanthology.org/2023.findings-acl.71/ | Anthology ID:2023.findings-acl.71 Volume:Findings of the Association for Computational Linguistics: ACL 2023Month:July Year:2023 Address:Toronto, Canada Editors:Anna Rogers, Jordan Boyd-Graber, Naoaki OkazakiVenue:FindingsSIG:Publisher:Association for Computational Linguistics Note:Pages:1102–1121 Language:URL:https://... |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | peng-etal-2024-answer | \cite{peng-etal-2024-answer} | Answer is All You Need: Instruction-following Text Embedding via
Answering the Question | http://arxiv.org/abs/2402.09642v1 | This work aims to build a text embedder that can capture characteristics of
texts specified by user instructions. Despite its tremendous potential to
deploy user-oriented embeddings, none of previous approaches provides a
concrete solution for it. This paper offers a new viewpoint, which treats the
instruction as a que... | true | true | Letian Peng and
Yuwei Zhang and
Zilong Wang and
Jayanth Srinivasa and
Gaowen Liu and
Zihan Wang and
Jingbo Shang | 2,024 | null | https://doi.org/10.18653/v1/2024.acl-long.27 | 10.18653/V1/2024.ACL-LONG.27 | null | Answer is All You Need: Instruction-following Text Embedding via
Answering the Question | Answer is All You Need: Instruction-following Text ... | https://aclanthology.org/2024.acl-long.27/ | by L Peng · 2024 · Cited by 11 — This work aims to build a text embedder that can capture characteristics of texts specified by user instructions clarifying the similarity criterion.See more |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | weller2024promptriever | \cite{weller2024promptriever} | Promptriever: Instruction-Trained Retrievers Can Be Prompted Like
Language Models | http://arxiv.org/abs/2409.11136v1 | Instruction-tuned language models (LM) are able to respond to imperative
commands, providing a more natural user interface compared to their base
counterparts. In this work, we present Promptriever, the first retrieval model
able to be prompted like an LM. To train Promptriever, we curate and release a
new instance-lev... | true | true | Orion Weller and
Benjamin Van Durme and
Dawn J. Lawrie and
Ashwin Paranjape and
Yuhao Zhang and
Jack Hessel | 2,025 | null | https://openreview.net/forum?id=odvSjn416y | null | null | Promptriever: Instruction-Trained Retrievers Can Be Prompted Like
Language Models | Promptriever: Instruction-Trained Retrievers Can Be ... | https://openreview.net/forum?id=odvSjn416y | by O Weller · Cited by 29 — This paper introduces Promptriever, a retrieval model that can be prompted like a language model. The authors construct an instance-level instruction training |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | min2024unihgkr | \cite{min2024unihgkr} | UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers | http://arxiv.org/abs/2410.20163v2 | Existing information retrieval (IR) models often assume a homogeneous
structure for knowledge sources and user queries, limiting their applicability
in real-world settings where retrieval is inherently heterogeneous and diverse.
In this paper, we introduce UniHGKR, a unified instruction-aware heterogeneous
knowledge re... | true | true | Dehai Min and
Zhiyang Xu and
Guilin Qi and
Lifu Huang and
Chenyu You | 2,025 | null | https://aclanthology.org/2025.naacl-long.234/ | null | null | UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers | UniHGKR: Unified Instruction-aware Heterogeneous ... | https://arxiv.org/abs/2410.20163 | by D Min · 2024 · Cited by 2 — In this paper, we introduce UniHGKR, a unified instruction-aware heterogeneous knowledge retriever that (1) builds a unified retrieval space for heterogeneous |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | oh2024instructir | \cite{oh2024instructir} | INSTRUCTIR: A Benchmark for Instruction Following of Information
Retrieval Models | http://arxiv.org/abs/2402.14334v1 | Despite the critical need to align search targets with users' intention,
retrievers often only prioritize query information without delving into the
users' intended search context. Enhancing the capability of retrievers to
understand intentions and preferences of users, akin to language model
instructions, has the pote... | true | true | Hanseok Oh and
Hyunji Lee and
Seonghyeon Ye and
Haebin Shin and
Hansol Jang and
Changwook Jun and
Minjoon Seo | 2,024 | null | https://doi.org/10.48550/arXiv.2402.14334 | 10.48550/ARXIV.2402.14334 | arXiv | INSTRUCTIR: A Benchmark for Instruction Following of Information
Retrieval Models | InstructIR: A Benchmark for Instruction Following of ... | https://arxiv.org/html/2402.14334v1 | Our approach focuses on user-aligned instructions tailored to each query instance, reflecting the diverse characteristics inherent in real-world search scenarios. Moreover, lack of benchmarks to evaluate retrievers on user-aligned scenarios prevents the mature discussions of instruction following in retrieval task. In ... |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | sun2024mair | \cite{sun2024mair} | MAIR: A Massive Benchmark for Evaluating Instructed Retrieval | http://arxiv.org/abs/2410.10127v1 | Recent information retrieval (IR) models are pre-trained and
instruction-tuned on massive datasets and tasks, enabling them to perform well
on a wide range of tasks and potentially generalize to unseen tasks with
instructions. However, existing IR benchmarks focus on a limited scope of
tasks, making them insufficient f... | true | true | Weiwei Sun and
Zhengliang Shi and
Wu Long and
Lingyong Yan and
Xinyu Ma and
Yiding Liu and
Min Cao and
Dawei Yin and
Zhaochun Ren | 2,024 | null | https://aclanthology.org/2024.emnlp-main.778 | null | null | MAIR: A Massive Benchmark for Evaluating Instructed Retrieval | MAIR: A Massive Benchmark for Evaluating Instructed Retrieval | http://arxiv.org/pdf/2410.10127v1 | Recent information retrieval (IR) models are pre-trained and
instruction-tuned on massive datasets and tasks, enabling them to perform well
on a wide range of tasks and potentially generalize to unseen tasks with
instructions. However, existing IR benchmarks focus on a limited scope of
tasks, making them insufficient f... |
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding
based on Guided Space Transformation | 2505.24754v1 | weller2024followir | \cite{weller2024followir} | FollowIR: Evaluating and Teaching Information Retrieval Models to Follow
Instructions | http://arxiv.org/abs/2403.15246v3 | Modern Language Models (LMs) are capable of following long and complex
instructions that enable a large and diverse set of user requests. While
Information Retrieval (IR) models use these LMs as the backbone of their
architectures, virtually none of them allow users to provide detailed
instructions alongside queries, t... | true | true | Orion Weller and
Benjamin Chang and
Sean MacAvaney and
Kyle Lo and
Arman Cohan and
Benjamin Van Durme and
Dawn J. Lawrie and
Luca Soldaini | 2,025 | null | https://aclanthology.org/2025.naacl-long.597/ | null | null | FollowIR: Evaluating and Teaching Information Retrieval Models to Follow
Instructions | FollowIR: Evaluating and Teaching Information Retrieval ... | https://arxiv.org/abs/2403.15246 | by O Weller · 2024 · Cited by 43 — Through this process, we can measure how well IR models follow instructions, through a new pairwise evaluation framework. Our results indicate |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | ladhak-etal-2020-exploring | \cite{ladhak-etal-2020-exploring} | Exploring Content Selection in Summarization of Novel Chapters | http://arxiv.org/abs/2005.01840v3 | We present a new summarization task, generating summaries of novel chapters
using summary/chapter pairs from online study guides. This is a harder task
than the news summarization task, given the chapter length as well as the
extreme paraphrasing and generalization found in the summaries. We focus on
extractive summari... | true | true | Ladhak, Faisal and
Li, Bryan and
Al-Onaizan, Yaser and
McKeown, Kathleen | 2,020 | null | https://aclanthology.org/2020.acl-main.453/ | 10.18653/v1/2020.acl-main.453 | null | Exploring Content Selection in Summarization of Novel Chapters | Exploring Content Selection in Summarization of Novel Chapters | http://arxiv.org/pdf/2005.01840v3 | We present a new summarization task, generating summaries of novel chapters
using summary/chapter pairs from online study guides. This is a harder task
than the news summarization task, given the chapter length as well as the
extreme paraphrasing and generalization found in the summaries. We focus on
extractive summari... |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | pu-etal-2022-two | \cite{pu-etal-2022-two} | Two-Stage Movie Script Summarization: An Efficient Method For Low-Resource Long Document Summarization | null | null | true | false | Liu, Dongqi and
Hong, Xudong and
Lin, Pin-Jie and
Chang, Ernie and
Demberg, Vera | 2,022 | null | https://aclanthology.org/2022.creativesumm-1.9/ | null | null | Two-Stage Movie Script Summarization: An Efficient Method For Low-Resource Long Document Summarization | Two-Stage Movie Script Summarization: An Efficient Method For ... | https://scispace.com/papers/two-stage-movie-script-summarization-an-efficient-method-for-2ca5vhpp | The core innovation in our model employs a two-stage hierarchical architecture for movie script summarization. In the first stage, a heuristic extraction method |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | gorinski-lapata-2015-movie | \cite{gorinski-lapata-2015-movie} | Movie Script Summarization as Graph-based Scene Extraction | null | null | true | false | Gorinski, Philip John and
Lapata, Mirella | 2,015 | null | https://aclanthology.org/N15-1113/ | 10.3115/v1/N15-1113 | null | Movie Script Summarization as Graph-based Scene Extraction | Movie Script Summarization As Graph-Based Scene Extraction | PDF | https://www.scribd.com/document/456741694/N15-1113 | The document discusses summarizing movie scripts by extracting a chain of important scenes. It formalizes script summarization as finding an optimal scene chain |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | saxena-keller-2024-select | \cite{saxena-keller-2024-select} | Select and Summarize: Scene Saliency for Movie Script Summarization | http://arxiv.org/abs/2404.03561v1 | Abstractive summarization for long-form narrative texts such as movie scripts
is challenging due to the computational and memory constraints of current
language models. A movie script typically comprises a large number of scenes;
however, only a fraction of these scenes are salient, i.e., important for
understanding th... | true | true | Saxena, Rohit and
Keller, Frank | 2,024 | null | https://aclanthology.org/2024.findings-naacl.218/ | 10.18653/v1/2024.findings-naacl.218 | null | Select and Summarize: Scene Saliency for Movie Script Summarization | Select and Summarize: Scene Saliency for Movie Script Summarization | http://arxiv.org/pdf/2404.03561v1 | Abstractive summarization for long-form narrative texts such as movie scripts
is challenging due to the computational and memory constraints of current
language models. A movie script typically comprises a large number of scenes;
however, only a fraction of these scenes are salient, i.e., important for
understanding th... |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | zaheer2020bigbird | \cite{zaheer2020bigbird} | Big Bird: Transformers for Longer Sequences | http://arxiv.org/abs/2007.14062v2 | Transformers-based models, such as BERT, have been one of the most successful
deep learning models for NLP. Unfortunately, one of their core limitations is
the quadratic dependency (mainly in terms of memory) on the sequence length due
to their full attention mechanism. To remedy this, we propose, BigBird, a
sparse att... | true | true | Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon, Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and Ahmed, Amr | 2,020 | null | https://proceedings.neurips.cc/paper_files/paper/2020/file/c8512d142a2d849725f31a9a7a361ab9-Paper.pdf | null | null | Big Bird: Transformers for Longer Sequences | Big Bird: Transformers for Longer Sequences | http://arxiv.org/pdf/2007.14062v2 | Transformers-based models, such as BERT, have been one of the most successful
deep learning models for NLP. Unfortunately, one of their core limitations is
the quadratic dependency (mainly in terms of memory) on the sequence length due
to their full attention mechanism. To remedy this, we propose, BigBird, a
sparse att... |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | Beltagy2020Longformer | \cite{Beltagy2020Longformer} | Longformer: The Long-Document Transformer | http://arxiv.org/abs/2004.05150v2 | Transformer-based models are unable to process long sequences due to their
self-attention operation, which scales quadratically with the sequence length.
To address this limitation, we introduce the Longformer with an attention
mechanism that scales linearly with sequence length, making it easy to process
documents of ... | true | true | Iz Beltagy and Matthew E. Peters and Arman Cohan | 2,020 | null | https://arxiv.org/abs/2004.05150 | null | null | Longformer: The Long-Document Transformer | [PDF] Longformer: The Long-Document Transformer | https://ysu1989.github.io/courses/au20/cse5539/Longformer.pdf | Longformer: The Long-Document Transformer Beltagy et al., 2020 Presented by Leslie Zhou Background ◦Transformers: have achieved state-of-the-art results in a wide range of natural language tasks including generative language modeling and discriminative language understanding. (2019)) ◦Classification (IMDB and Hyperpart... |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | kitaev2020reformerefficienttransformer | \cite{kitaev2020reformerefficienttransformer} | Reformer: The Efficient Transformer | http://arxiv.org/abs/2001.04451v2 | Large Transformer models routinely achieve state-of-the-art results on a
number of tasks but training these models can be prohibitively costly,
especially on long sequences. We introduce two techniques to improve the
efficiency of Transformers. For one, we replace dot-product attention by one
that uses locality-sensiti... | true | true | Nikita Kitaev and Łukasz Kaiser and Anselm Levskaya | 2,020 | null | https://arxiv.org/abs/2001.04451 | null | null | Reformer: The Efficient Transformer | Reformer: The Efficient Transformer | http://arxiv.org/pdf/2001.04451v2 | Large Transformer models routinely achieve state-of-the-art results on a
number of tasks but training these models can be prohibitively costly,
especially on long sequences. We introduce two techniques to improve the
efficiency of Transformers. For one, we replace dot-product attention by one
that uses locality-sensiti... |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | guo-etal-2022-longt5 | \cite{guo-etal-2022-longt5} | {L}ong{T}5: {E}fficient Text-To-Text Transformer for Long Sequences | null | null | true | false | Guo, Mandy and
Ainslie, Joshua and
Uthus, David and
Ontanon, Santiago and
Ni, Jianmo and
Sung, Yun-Hsuan and
Yang, Yinfei | 2,022 | null | https://aclanthology.org/2022.findings-naacl.55/ | 10.18653/v1/2022.findings-naacl.55 | null | {L}ong{T}5: {E}fficient Text-To-Text Transformer for Long Sequences | LongT5: Efficient Text-To-Text Transformer for Long Sequences | https://aclanthology.org/2022.findings-naacl.55/ | In this paper, we present LongT5, a new model that explores the effects of scaling both the input length and model size at the same time. |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | wang2020linformerselfattentionlinearcomplexity | \cite{wang2020linformerselfattentionlinearcomplexity} | Linformer: Self-Attention with Linear Complexity | http://arxiv.org/abs/2006.04768v3 | Large transformer models have shown extraordinary success in achieving
state-of-the-art results in many natural language processing applications.
However, training and deploying these models can be prohibitively costly for
long sequences, as the standard self-attention mechanism of the Transformer
uses $O(n^2)$ time an... | true | true | Sinong Wang and Belinda Z. Li and Madian Khabsa and Han Fang and Hao Ma | 2,020 | null | https://arxiv.org/abs/2006.04768 | null | null | Linformer: Self-Attention with Linear Complexity | [2006.04768] Linformer: Self-Attention with Linear Complexity | https://arxiv.org/abs/2006.04768 | by S Wang · 2020 · Cited by 2185 — A new self-attention mechanism, which reduces the overall self-attention complexity from O(n^2) to O(n) in both time and space. |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | chen2023extendingcontextwindowlarge | \cite{chen2023extendingcontextwindowlarge} | Extending Context Window of Large Language Models via Positional
Interpolation | http://arxiv.org/abs/2306.15595v2 | We present Position Interpolation (PI) that extends the context window sizes
of RoPE-based pretrained LLMs such as LLaMA models to up to 32768 with minimal
fine-tuning (within 1000 steps), while demonstrating strong empirical results
on various tasks that require long context, including passkey retrieval,
language mode... | true | true | Shouyuan Chen and Sherman Wong and Liangjian Chen and Yuandong Tian | 2,023 | null | https://arxiv.org/abs/2306.15595 | null | null | Extending Context Window of Large Language Models via Positional
Interpolation | Extending Context Window of Large Language Models via ... - arXiv | https://arxiv.org/abs/2306.15595 | We present Position Interpolation (PI) that extends the context window sizes of RoPE-based pretrained LLMs such as LLaMA models to up to 32768 with minimal |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | gpt4_technical | \cite{gpt4_technical} | GPT-4 Technical Report | null | null | true | false | OpenAI | 2,023 | null | null | null | arXiv preprint arXiv:2303.08774 | GPT-4 Technical Report | GPT-4 Technical Report | http://arxiv.org/pdf/2303.08774v6 | We report the development of GPT-4, a large-scale, multimodal model which can
accept image and text inputs and produce text outputs. While less capable than
humans in many real-world scenarios, GPT-4 exhibits human-level performance on
various professional and academic benchmarks, including passing a simulated bar
exam... |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | mistralai2024large | \cite{mistralai2024large} | Large Enough | null | null | true | false | {Mistral AI} | 2,024 | null | https://mistral.ai/news/mistral-large-2407/ | null | null | Large Enough | is large enough | Meaning, Grammar Guide & Usage Examples | https://ludwig.guru/s/is+large+enough | "is large enough" is correct and usable in written English. You can use it when you need to express that an object, quantity, or area of space is greater than |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | liu-etal-2024-lost | \cite{liu-etal-2024-lost} | Lost in the Middle: How Language Models Use Long Contexts | http://arxiv.org/abs/2307.03172v3 | While recent language models have the ability to take long contexts as input,
relatively little is known about how well they use longer context. We analyze
the performance of language models on two tasks that require identifying
relevant information in their input contexts: multi-document question answering
and key-val... | true | true | Liu, Nelson F. and
Lin, Kevin and
Hewitt, John and
Paranjape, Ashwin and
Bevilacqua, Michele and
Petroni, Fabio and
Liang, Percy | 2,024 | null | https://aclanthology.org/2024.tacl-1.9/ | 10.1162/tacl_a_00638 | Transactions of the Association for Computational Linguistics | Lost in the Middle: How Language Models Use Long Contexts | Lost in the Middle: How Language Models Use Long Contexts | http://arxiv.org/pdf/2307.03172v3 | While recent language models have the ability to take long contexts as input,
relatively little is known about how well they use longer context. We analyze
the performance of language models on two tasks that require identifying
relevant information in their input contexts: multi-document question answering
and key-val... |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | ivgi-etal-2023-sled | \cite{ivgi-etal-2023-sled} | Efficient Long-Text Understanding with Short-Text Models | http://arxiv.org/abs/2208.00748v3 | Transformer-based pretrained language models (LMs) are ubiquitous across
natural language understanding, but cannot be applied to long sequences such as
stories, scientific articles and long documents, due to their quadratic
complexity. While a myriad of efficient transformer variants have been
proposed, they are typic... | true | true | Ivgi, Maor and
Shaham, Uri and
Berant, Jonathan | 2,023 | null | https://aclanthology.org/2023.tacl-1.17/ | 10.1162/tacl_a_00547 | Transactions of the Association for Computational Linguistics | Efficient Long-Text Understanding with Short-Text Models | Efficient Long-Text Understanding with Short-Text Models | https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00547/115346/Efficient-Long-Text-Understanding-with-Short-Text | In this work we present SLED, a simple approach for modeling long texts that slides a pretrained short-range encoder over a long input document |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | bertsch2023unlimiformer | \cite{bertsch2023unlimiformer} | Unlimiformer: Long-Range Transformers with Unlimited Length Input | http://arxiv.org/abs/2305.01625v3 | Since the proposal of transformers, these models have been limited to bounded
input lengths, because of their need to attend to every token in the input. In
this work, we propose Unlimiformer: a general approach that wraps any existing
pretrained encoder-decoder transformer, and offloads the cross-attention
computation... | true | true | Amanda Bertsch and Uri Alon and Graham Neubig and Matthew R. Gormley | 2,023 | null | https://openreview.net/forum?id=lJWUJWLCJo | null | null | Unlimiformer: Long-Range Transformers with Unlimited Length Input | Public repo for the NeurIPS 2023 paper "Unlimiformer | https://github.com/abertsch72/unlimiformer | Unlimiformer: Long-Range Transformers with Unlimited Length Input (NeurIPS 2023) ... Unlimiformer is a method for augmenting pretrained encoder-decoder models |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | saxena2025endtoendlongdocumentsummarization | \cite{saxena2025endtoendlongdocumentsummarization} | End-to-End Long Document Summarization using Gradient Caching | http://arxiv.org/abs/2501.01805v2 | Training transformer-based encoder-decoder models for long document
summarization poses a significant challenge due to the quadratic memory
consumption during training. Several approaches have been proposed to extend
the input length at test time, but training with these approaches is still
difficult, requiring truncat... | true | true | Rohit Saxena and Hao Tang and Frank Keller | 2,025 | null | https://arxiv.org/abs/2501.01805 | null | null | End-to-End Long Document Summarization using Gradient Caching | [Literature Review] End-to-End Long Document ... | https://www.themoonlight.io/en/review/end-to-end-long-document-summarization-using-gradient-caching | This page provides the most accurate and concise summary worldwide for the paper titled End-to-End Long Document Summarization using Gradient Caching. With |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | zhang2024chain | \cite{zhang2024chain} | Chain of Agents: Large Language Models Collaborating on Long-Context
Tasks | http://arxiv.org/abs/2406.02818v1 | Addressing the challenge of effectively processing long contexts has become a
critical issue for Large Language Models (LLMs). Two common strategies have
emerged: 1) reducing the input length, such as retrieving relevant chunks by
Retrieval-Augmented Generation (RAG), and 2) expanding the context window limit
of LLMs. ... | true | true | Yusen Zhang and Ruoxi Sun and Yanfei Chen and Tomas Pfister and Rui Zhang and Sercan O Arik | 2,024 | null | https://openreview.net/forum?id=LuCLf4BJsr | null | null | Chain of Agents: Large Language Models Collaborating on Long-Context
Tasks | Chain of Agents: Large Language Models Collaborating ... | https://arxiv.org/abs/2406.02818 | View Jobs Skip to main content arXiv Is Hiring a DevOps Engineer View Jobs We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors.Donate >cs> arXiv:2406.02818 Help | Advanced Search Search GO quick links Login Help Pages About Computer Science > Computation and Language ... |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | chang2024booookscore | \cite{chang2024booookscore} | BooookScore: A systematic exploration of book-length summarization in
the era of LLMs | http://arxiv.org/abs/2310.00785v4 | Summarizing book-length documents (>100K tokens) that exceed the context
window size of large language models (LLMs) requires first breaking the input
document into smaller chunks and then prompting an LLM to merge, update, and
compress chunk-level summaries. Despite the complexity and importance of this
task, it has y... | true | true | Yapei Chang and
Kyle Lo and
Tanya Goyal and
Mohit Iyyer | 2,024 | null | https://openreview.net/forum?id=7Ttk3RzDeu | null | null | BooookScore: A systematic exploration of book-length summarization in
the era of LLMs | lilakk/BooookScore - GitHub | https://github.com/lilakk/BooookScore | Official package for our ICLR 2024 paper, "BooookScore: A systematic exploration of book-length summarization in the era of LLMs". arxiv.org/abs/2310.00785 |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | jeong2025agentasjudgefactualsummarizationlong | \cite{jeong2025agentasjudgefactualsummarizationlong} | Agent-as-Judge for Factual Summarization of Long Narratives | http://arxiv.org/abs/2501.09993v1 | Large Language Models (LLMs) have demonstrated near-human performance in
summarization tasks based on traditional metrics such as ROUGE and BERTScore.
However, these metrics do not adequately capture critical aspects of
summarization quality, such as factual accuracy, particularly for long
narratives (>100K tokens). Re... | true | true | Yeonseok Jeong and Minsoo Kim and Seung-won Hwang and Byung-Hak Kim | 2,025 | null | https://arxiv.org/abs/2501.09993 | null | null | Agent-as-Judge for Factual Summarization of Long Narratives | YeonseokJeong/NarrativeFactScore: Agent-as-Judge for ... | https://github.com/YeonseokJeong/NarrativeFactScore | NarrativeFactScore is a novel "Agent-as-a-Judge" framework for evaluating and refining summaries of long narratives. The framework provides factual |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | NEURIPS2020_rag | \cite{NEURIPS2020_rag} | Advances in Neural Information Processing Systems 33, NeurIPS 2020 | null | null | true | false | Lewis, Patrick and Perez, Ethan and Piktus, Aleksandra and Petroni, Fabio and Karpukhin, Vladimir and Goyal, Naman and K\"{u}ttler, Heinrich and Lewis, Mike and Yih, Wen-tau and Rockt\"{a}schel, Tim and Riedel, Sebastian and Kiela, Douwe | 2,020 | null | https://proceedings.neurips.cc/paper_files/paper/2020/file/6b493230205f780e1bc26945df7481e5-Paper.pdf | null | null | Advances in Neural Information Processing Systems 33, NeurIPS 2020 | Book - NIPS | https://papers.nips.cc/paper/2020 | Advances in Neural Information Processing Systems 33 (NeurIPS 2020) ; A graph similarity for deep learning Seongmin Ok ; An Unsupervised Information-Theoretic |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | geng-etal-2022-improving-abstractive | \cite{geng-etal-2022-improving-abstractive} | Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning | null | null | true | false | Geng, Zhichao and
Zhong, Ming and
Yin, Zhangyue and
Qiu, Xipeng and
Huang, Xuanjing | 2,022 | null | https://aclanthology.org/2022.coling-1.569/ | null | null | Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning | Improving Abstractive Dialogue Summarization with ... | https://aclanthology.org/2022.coling-1.569.pdf | by Z Geng · 2022 · Cited by 12 — We propose three speaker-aware su- pervised contrastive learning tasks: Token-level. SCL, Turn-level SCL, and Global-level SCL. By jointly |
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization | 2505.24575v1 | uthus-ni-2023-rise | \cite{uthus-ni-2023-rise} | RISE: Leveraging Retrieval Techniques for Summarization Evaluation | http://arxiv.org/abs/2212.08775v2 | Evaluating automatically-generated text summaries is a challenging task.
While there have been many interesting approaches, they still fall short of
human evaluations. We present RISE, a new approach for evaluating summaries by
leveraging techniques from information retrieval. RISE is first trained as a
retrieval task ... | true | true | Uthus, David and
Ni, Jianmo | 2,023 | null | https://aclanthology.org/2023.findings-acl.865/ | 10.18653/v1/2023.findings-acl.865 | null | RISE: Leveraging Retrieval Techniques for Summarization Evaluation | RISE: Leveraging Retrieval Techniques for Summarization Evaluation | http://arxiv.org/pdf/2212.08775v2 | Evaluating automatically-generated text summaries is a challenging task.
While there have been many interesting approaches, they still fall short of
human evaluations. We present RISE, a new approach for evaluating summaries by
leveraging techniques from information retrieval. RISE is first trained as a
retrieval task ... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | ouyang2022traininglanguagemodelsfollow | \cite{ouyang2022traininglanguagemodelsfollow} | Training language models to follow instructions with human feedback | null | null | true | false | Long Ouyang and
Jeffrey Wu and
Xu Jiang and
Diogo Almeida and
Carroll L. Wainwright and
Pamela Mishkin and
Chong Zhang and
Sandhini Agarwal and
Katarina Slama and
Alex Ray and
John Schulman and
Jacob Hilton and
Fraser Kelton and
Luke Miller and
Maddie Simens and
Amanda Askell and
Peter Welinder and
Paul F. Christiano a... | 2,022 | null | http://papers.nips.cc/paper\_files/paper/2022/hash/b1efde53be364a73914f58805a001731-Abstract-Conference.html | null | null | Training language models to follow instructions with human feedback | Training language models to follow instructions with human feedback | http://arxiv.org/pdf/2203.02155v1 | Making language models bigger does not inherently make them better at
following a user's intent. For example, large language models can generate
outputs that are untruthful, toxic, or simply not helpful to the user. In other
words, these models are not aligned with their users. In this paper, we show an
avenue for alig... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | bai2022traininghelpfulharmlessassistant | \cite{bai2022traininghelpfulharmlessassistant} | Training a Helpful and Harmless Assistant with Reinforcement Learning
from Human Feedback | http://arxiv.org/abs/2204.05862v1 | We apply preference modeling and reinforcement learning from human feedback
(RLHF) to finetune language models to act as helpful and harmless assistants.
We find this alignment training improves performance on almost all NLP
evaluations, and is fully compatible with training for specialized skills such
as python coding... | true | true | Yuntao Bai and Andy Jones and Kamal Ndousse and Amanda Askell and Anna Chen and Nova DasSarma and Dawn Drain and Stanislav Fort and Deep Ganguli and Tom Henighan and Nicholas Joseph and Saurav Kadavath and Jackson Kernion and Tom Conerly and Sheer El-Showk and Nelson Elhage and Zac Hatfield-Dodds and Danny Hernandez an... | 2,022 | null | https://arxiv.org/abs/2204.05862 | null | ArXiv preprint | Training a Helpful and Harmless Assistant with Reinforcement Learning
from Human Feedback | Training a Helpful and Harmless Assistant with Reinforcement ... | https://arxiv.org/abs/2204.05862 | [2204.05862] Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback Title:Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback View a PDF of the paper titled Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback,... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | ganguli2022redteaminglanguagemodels | \cite{ganguli2022redteaminglanguagemodels} | Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors,
and Lessons Learned | http://arxiv.org/abs/2209.07858v2 | We describe our early efforts to red team language models in order to
simultaneously discover, measure, and attempt to reduce their potentially
harmful outputs. We make three main contributions. First, we investigate
scaling behaviors for red teaming across 3 model sizes (2.7B, 13B, and 52B
parameters) and 4 model type... | true | true | Deep Ganguli and Liane Lovitt and Jackson Kernion and Amanda Askell and Yuntao Bai and Saurav Kadavath and Ben Mann and Ethan Perez and Nicholas Schiefer and Kamal Ndousse and Andy Jones and Sam Bowman and Anna Chen and Tom Conerly and Nova DasSarma and Dawn Drain and Nelson Elhage and Sheer El-Showk and Stanislav Fort... | 2,022 | null | https://arxiv.org/abs/2209.07858 | null | ArXiv preprint | Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors,
and Lessons Learned | (PDF) Red Teaming Language Models to Reduce Harms | https://www.researchgate.net/publication/363651560_Red_Teaming_Language_Models_to_Reduce_Harms_Methods_Scaling_Behaviors_and_Lessons_Learned | Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned. August 2022. DOI:10.48550/arXiv.2209.07858. |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | lermen2024lorafinetuningefficientlyundoes | \cite{lermen2024lorafinetuningefficientlyundoes} | LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B | http://arxiv.org/abs/2310.20624v2 | AI developers often apply safety alignment procedures to prevent the misuse
of their AI systems. For example, before Meta released Llama 2-Chat - a
collection of instruction fine-tuned large language models - they invested
heavily in safety training, incorporating extensive red-teaming and
reinforcement learning from h... | true | true | Simon Lermen and Charlie Rogers-Smith and Jeffrey Ladish | 2,023 | null | https://arxiv.org/abs/2310.20624 | null | ArXiv preprint | LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B | Paper page - LoRA Fine-tuning Efficiently Undoes Safety ... | https://huggingface.co/papers/2310.20624 | We achieve a refusal rate below 1% for our 70B Llama 2-Chat model on two refusal benchmarks. Our fine-tuning method retains general performance, |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | yang2023shadowalignmenteasesubverting | \cite{yang2023shadowalignmenteasesubverting} | Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models | http://arxiv.org/abs/2310.02949v1 | Warning: This paper contains examples of harmful language, and reader
discretion is recommended. The increasing open release of powerful large
language models (LLMs) has facilitated the development of downstream
applications by reducing the essential cost of data annotation and computation.
To ensure AI safety, extensi... | true | true | Xianjun Yang and Xiao Wang and Qi Zhang and Linda Petzold and William Yang Wang and Xun Zhao and Dahua Lin | 2,023 | null | https://arxiv.org/abs/2310.02949 | null | ArXiv preprint | Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models | The Ease of Subverting Safely-Aligned Language Models | https://openreview.net/forum?id=rg0vQmkB7F | The paper identifies a new attack, termed "Shadow Alignment", that undermines the safety measures of large language models (LLMs) with minimal |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | qi2023finetuningalignedlanguagemodels | \cite{qi2023finetuningalignedlanguagemodels} | Fine-tuning Aligned Language Models Compromises Safety, Even When
Users Do Not Intend To! | null | null | true | false | Xiangyu Qi and
Yi Zeng and
Tinghao Xie and
Pin{-}Yu Chen and
Ruoxi Jia and
Prateek Mittal and
Peter Henderson | 2,024 | null | https://openreview.net/forum?id=hTEGyKf0dZ | null | null | Fine-tuning Aligned Language Models Compromises Safety, Even When
Users Do Not Intend To! | Fine-tuning Aligned Language Models Compromises ... | https://openreview.net/forum?id=Xaf289hqmZ | por X Qi · 2024 · Mencionado por 717 — Fine-tuning aligned language models compromises safety, even when users do not intend to! Open Webpage Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | andriushchenko2024jailbreaking | \cite{andriushchenko2024jailbreaking} | Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks | http://arxiv.org/abs/2404.02151v4 | We show that even the most recent safety-aligned LLMs are not robust to
simple adaptive jailbreaking attacks. First, we demonstrate how to successfully
leverage access to logprobs for jailbreaking: we initially design an
adversarial prompt template (sometimes adapted to the target LLM), and then we
apply random search ... | true | true | Andriushchenko, Maksym and Croce, Francesco and Flammarion, Nicolas | 2,024 | null | https://arxiv.org/abs/2404.02151 | null | ArXiv preprint | Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks | Jailbreaking Leading Safety-Aligned LLMs with Simple ... | https://openreview.net/forum?id=hXA8wqRdyV | by M Andriushchenko · Cited by 229 — This paper proposes an adaptive jailbreaking attack, which aims at attacking safety-aligned language models (LLMs), demonstrating that even the latest models |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | zou2023universaltransferableadversarialattacks | \cite{zou2023universaltransferableadversarialattacks} | Universal and Transferable Adversarial Attacks on Aligned Language Models | null | null | true | false | Andy Zou and Zifan Wang and Nicholas Carlini and Milad Nasr and J. Zico Kolter and Matt Fredrikson | 2,023 | null | https://arxiv.org/abs/2307.15043 | null | ArXiv preprint | Universal and Transferable Adversarial Attacks on Aligned Language Models | Universal and Transferable Adversarial Attacks on Aligned Language Models | http://arxiv.org/pdf/2307.15043v2 | Because "out-of-the-box" large language models are capable of generating a
great deal of objectionable content, recent work has focused on aligning these
models in an attempt to prevent undesirable generation. While there has been
some success at circumventing these measures -- so-called "jailbreaks" against
LLMs -- th... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | chao2024jailbreakingblackboxlarge | \cite{chao2024jailbreakingblackboxlarge} | Jailbreaking Black Box Large Language Models in Twenty Queries | null | null | true | false | Patrick Chao and Alexander Robey and Edgar Dobriban and Hamed Hassani and George J. Pappas and Eric Wong | 2,023 | null | https://arxiv.org/abs/2310.08419 | null | ArXiv preprint | Jailbreaking Black Box Large Language Models in Twenty Queries | Jailbreaking Black Box Large Language Models in Twenty Queries | http://arxiv.org/pdf/2310.08419v4 | There is growing interest in ensuring that large language models (LLMs) align
with human values. However, the alignment of such models is vulnerable to
adversarial jailbreaks, which coax LLMs into overriding their safety
guardrails. The identification of these vulnerabilities is therefore
instrumental in understanding ... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | weidinger2021ethicalsocialrisksharm | \cite{weidinger2021ethicalsocialrisksharm} | Ethical and social risks of harm from Language Models | http://arxiv.org/abs/2112.04359v1 | This paper aims to help structure the risk landscape associated with
large-scale Language Models (LMs). In order to foster advances in responsible
innovation, an in-depth understanding of the potential risks posed by these
models is needed. A wide range of established and anticipated risks are
analysed in detail, drawi... | true | true | Laura Weidinger and John Mellor and Maribeth Rauh and Conor Griffin and Jonathan Uesato and Po-Sen Huang and Myra Cheng and Mia Glaese and Borja Balle and Atoosa Kasirzadeh and Zac Kenton and Sasha Brown and Will Hawkins and Tom Stepleton and Courtney Biles and Abeba Birhane and Julia Haas and Laura Rimell and Lisa Ann... | 2,021 | null | https://arxiv.org/abs/2112.04359 | null | ArXiv preprint | Ethical and social risks of harm from Language Models | Ethical and social risks of harm from Language Models | http://arxiv.org/pdf/2112.04359v1 | This paper aims to help structure the risk landscape associated with
large-scale Language Models (LMs). In order to foster advances in responsible
innovation, an in-depth understanding of the potential risks posed by these
models is needed. A wide range of established and anticipated risks are
analysed in detail, drawi... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | arditi2024refusallanguagemodelsmediated | \cite{arditi2024refusallanguagemodelsmediated} | Refusal in Language Models Is Mediated by a Single Direction | http://arxiv.org/abs/2406.11717v3 | Conversational large language models are fine-tuned for both
instruction-following and safety, resulting in models that obey benign requests
but refuse harmful ones. While this refusal behavior is widespread across chat
models, its underlying mechanisms remain poorly understood. In this work, we
show that refusal is me... | true | true | Andy Arditi and
Oscar Obeso and
Aaquib Syed and
Daniel Paleka and
Nina Panickssery and
Wes Gurnee and
Neel Nanda | 2,024 | null | http://papers.nips.cc/paper\_files/paper/2024/hash/f545448535dfde4f9786555403ab7c49-Abstract-Conference.html | null | null | Refusal in Language Models Is Mediated by a Single Direction | Refusal in Language Models Is Mediated by a Single Direction | http://arxiv.org/pdf/2406.11717v3 | Conversational large language models are fine-tuned for both
instruction-following and safety, resulting in models that obey benign requests
but refuse harmful ones. While this refusal behavior is widespread across chat
models, its underlying mechanisms remain poorly understood. In this work, we
show that refusal is me... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | marshall2024refusalllmsaffinefunction | \cite{marshall2024refusalllmsaffinefunction} | Refusal in LLMs is an Affine Function | http://arxiv.org/abs/2411.09003v3 | We propose affine concept editing (ACE) as an approach for steering language
models' behavior by intervening directly in activations. We begin with an
affine decomposition of model activation vectors and show that prior methods
for steering model behavior correspond to subsets of terms of this
decomposition. We then pr... | true | true | Thomas Marshall and Adam Scherlis and Nora Belrose | 2,024 | null | https://arxiv.org/abs/2411.09003 | null | ArXiv preprint | Refusal in LLMs is an Affine Function | Refusal in LLMs is an Affine Function | http://arxiv.org/pdf/2411.09003v3 | We propose affine concept editing (ACE) as an approach for steering language
models' behavior by intervening directly in activations. We begin with an
affine decomposition of model activation vectors and show that prior methods
for steering model behavior correspond to subsets of terms of this
decomposition. We then pr... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | zou2023representationengineeringtopdownapproach | \cite{zou2023representationengineeringtopdownapproach} | Representation Engineering: A Top-Down Approach to AI Transparency | http://arxiv.org/abs/2310.01405v4 | In this paper, we identify and characterize the emerging area of
representation engineering (RepE), an approach to enhancing the transparency of
AI systems that draws on insights from cognitive neuroscience. RepE places
population-level representations, rather than neurons or circuits, at the
center of analysis, equipp... | true | true | Andy Zou and Long Phan and Sarah Chen and James Campbell and Phillip Guo and Richard Ren and Alexander Pan and Xuwang Yin and Mantas Mazeika and Ann-Kathrin Dombrowski and Shashwat Goel and Nathaniel Li and Michael J. Byun and Zifan Wang and Alex Mallen and Steven Basart and Sanmi Koyejo and Dawn Song and Matt Fredriks... | 2,023 | null | https://arxiv.org/abs/2310.01405 | null | ArXiv preprint | Representation Engineering: A Top-Down Approach to AI Transparency | Representation Engineering: A Top-Down Approach to AI ... | https://montrealethics.ai/representation-engineering-a-top-down-approach-to-ai-transparency/ | RepE is a top-down approach to transparency research that treats representations as the fundamental unit of analysis, aiming to understand and control |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | Spectralediting | \cite{Spectralediting} | Spectral Editing of Activations for Large Language Model Alignment | http://arxiv.org/abs/2405.09719v3 | Large language models (LLMs) often exhibit undesirable behaviours, such as
generating untruthful or biased content. Editing their internal representations
has been shown to be effective in mitigating such behaviours on top of the
existing alignment methods. We propose a novel inference-time editing method,
namely spect... | true | true | Yifu Qiu and
Zheng Zhao and
Yftah Ziser and
Anna Korhonen and
Edoardo Maria Ponti and
Shay B. Cohen | 2,024 | null | http://papers.nips.cc/paper\_files/paper/2024/hash/684c59d614fe6ae74a3be8c3ef07e061-Abstract-Conference.html | null | null | Spectral Editing of Activations for Large Language Model Alignment | Spectral Editing of Activations for Large Language Model Alignment | http://arxiv.org/pdf/2405.09719v3 | Large language models (LLMs) often exhibit undesirable behaviours, such as
generating untruthful or biased content. Editing their internal representations
has been shown to be effective in mitigating such behaviours on top of the
existing alignment methods. We propose a novel inference-time editing method,
namely spect... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | bhattacharjee2024inferencetimecategorywisesafetysteering | \cite{bhattacharjee2024inferencetimecategorywisesafetysteering} | Towards Inference-time Category-wise Safety Steering for Large Language
Models | http://arxiv.org/abs/2410.01174v1 | While large language models (LLMs) have seen unprecedented advancements in
capabilities and applications across a variety of use-cases, safety alignment
of these models is still an area of active research. The fragile nature of
LLMs, even models that have undergone extensive alignment and safety training
regimes, warra... | true | true | Amrita Bhattacharjee and Shaona Ghosh and Traian Rebedea and Christopher Parisien | 2,024 | null | https://arxiv.org/abs/2410.01174 | null | ArXiv preprint | Towards Inference-time Category-wise Safety Steering for Large Language
Models | Towards Inference-time Category-wise Safety Steering for Large... | https://openreview.net/forum?id=EkQRNLPFcn | We propose and explore an inference-time safety steering method for LLMs by intervening using category-specific steering vectors computed using model |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | uppaal2025profs | \cite{uppaal2025profs} | Model Editing as a Robust and Denoised variant of DPO: A Case Study on
Toxicity | http://arxiv.org/abs/2405.13967v5 | Recent alignment algorithms such as direct preference optimization (DPO) have
been developed to improve the safety of large language models (LLMs) by
training these models to match human behaviors exemplified by preference data.
However, these methods are both computationally intensive and lacking in
controllability an... | true | true | Uppaal, Rheeya and Dey, Apratim and He, Yiting and Zhong, Yiqiao and Hu, Junjie | 2,025 | null | null | null | null | Model Editing as a Robust and Denoised variant of DPO: A Case Study on
Toxicity | Rheeya Uppaal - Google Scholar | https://scholar.google.com/citations?user=nx3vmEkAAAAJ&hl=en | DeTox: Toxic Subspace Projection for Model Editing. R Uppaal, A De ... 2019. Model editing as a robust and denoised variant of dpo: A case study on toxicity. |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | burns2024discoveringlatentknowledgelanguage | \cite{burns2024discoveringlatentknowledgelanguage} | Discovering Latent Knowledge in Language Models Without Supervision | http://arxiv.org/abs/2212.03827v2 | Existing techniques for training language models can be misaligned with the
truth: if we train models with imitation learning, they may reproduce errors
that humans make; if we train them to generate text that humans rate highly,
they may output errors that human evaluators can't detect. We propose
circumventing this i... | true | true | Collin Burns and
Haotian Ye and
Dan Klein and
Jacob Steinhardt | 2,023 | null | https://openreview.net/pdf?id=ETKGuby0hcs | null | null | Discovering Latent Knowledge in Language Models Without Supervision | Discovering Latent Knowledge in Language Models Without Supervision | http://arxiv.org/pdf/2212.03827v2 | Existing techniques for training language models can be misaligned with the
truth: if we train models with imitation learning, they may reproduce errors
that humans make; if we train them to generate text that humans rate highly,
they may output errors that human evaluators can't detect. We propose
circumventing this i... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | panickssery2024steeringllama2contrastive | \cite{panickssery2024steeringllama2contrastive} | Steering Llama 2 via Contrastive Activation Addition | http://arxiv.org/abs/2312.06681v4 | We introduce Contrastive Activation Addition (CAA), an innovative method for
steering language models by modifying their activations during forward passes.
CAA computes "steering vectors" by averaging the difference in residual stream
activations between pairs of positive and negative examples of a particular
behavior,... | true | true | Nina Panickssery and Nick Gabrieli and Julian Schulz and Meg Tong and Evan Hubinger and Alexander Matt Turner | 2,023 | null | https://arxiv.org/abs/2312.06681 | null | ArXiv preprint | Steering Llama 2 via Contrastive Activation Addition | Steering Llama 2 via Contrastive Activation Addition | http://arxiv.org/pdf/2312.06681v4 | We introduce Contrastive Activation Addition (CAA), an innovative method for
steering language models by modifying their activations during forward passes.
CAA computes "steering vectors" by averaging the difference in residual stream
activations between pairs of positive and negative examples of a particular
behavior,... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | turner2024steeringlanguagemodelsactivation | \cite{turner2024steeringlanguagemodelsactivation} | Steering Language Models With Activation Engineering | http://arxiv.org/abs/2308.10248v5 | Prompt engineering and finetuning aim to maximize language model performance
on a given metric (like toxicity reduction). However, these methods do not
fully elicit a model's capabilities. To reduce this gap, we introduce
activation engineering: the inference-time modification of activations in order
to control (or ste... | true | true | Alexander Matt Turner and Lisa Thiergart and Gavin Leech and David Udell and Juan J. Vazquez and Ulisse Mini and Monte MacDiarmid | 2,023 | null | https://arxiv.org/abs/2308.10248 | null | ArXiv preprint | Steering Language Models With Activation Engineering | Steering Language Models With Activation Engineering | http://arxiv.org/pdf/2308.10248v5 | Prompt engineering and finetuning aim to maximize language model performance
on a given metric (like toxicity reduction). However, these methods do not
fully elicit a model's capabilities. To reduce this gap, we introduce
activation engineering: the inference-time modification of activations in order
to control (or ste... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | lee2025programmingrefusalconditionalactivation | \cite{lee2025programmingrefusalconditionalactivation} | Programming Refusal with Conditional Activation Steering | http://arxiv.org/abs/2409.05907v3 | LLMs have shown remarkable capabilities, but precisely controlling their
response behavior remains challenging. Existing activation steering methods
alter LLM behavior indiscriminately, limiting their practical applicability in
settings where selective responses are essential, such as content moderation or
domain-speci... | true | true | Bruce W. Lee and Inkit Padhi and Karthikeyan Natesan Ramamurthy and Erik Miehling and Pierre Dognin and Manish Nagireddy and Amit Dhurandhar | 2,024 | null | https://arxiv.org/abs/2409.05907 | null | ArXiv preprint | Programming Refusal with Conditional Activation Steering | Programming Refusal with Conditional Activation Steering | http://arxiv.org/pdf/2409.05907v3 | LLMs have shown remarkable capabilities, but precisely controlling their
response behavior remains challenging. Existing activation steering methods
alter LLM behavior indiscriminately, limiting their practical applicability in
settings where selective responses are essential, such as content moderation or
domain-speci... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | guerner2024geometricnotioncausalprobing | \cite{guerner2024geometricnotioncausalprobing} | A Geometric Notion of Causal Probing | http://arxiv.org/abs/2307.15054v4 | The linear subspace hypothesis (Bolukbasi et al., 2016) states that, in a
language model's representation space, all information about a concept such as
verbal number is encoded in a linear subspace. Prior work has relied on
auxiliary classification tasks to identify and evaluate candidate subspaces
that might give sup... | true | true | Clément Guerner and Anej Svete and Tianyu Liu and Alexander Warstadt and Ryan Cotterell | 2,023 | null | https://arxiv.org/abs/2307.15054 | null | ArXiv preprint | A Geometric Notion of Causal Probing | A Geometric Notion of Causal Probing | http://arxiv.org/pdf/2307.15054v4 | The linear subspace hypothesis (Bolukbasi et al., 2016) states that, in a
language model's representation space, all information about a concept such as
verbal number is encoded in a linear subspace. Prior work has relied on
auxiliary classification tasks to identify and evaluate candidate subspaces
that might give sup... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | haghighatkhah2022betterhitnailhead | \cite{haghighatkhah2022betterhitnailhead} | Better Hit the Nail on the Head than Beat around the Bush: Removing
Protected Attributes with a Single Projection | http://arxiv.org/abs/2212.04273v1 | Bias elimination and recent probing studies attempt to remove specific
information from embedding spaces. Here it is important to remove as much of
the target information as possible, while preserving any other information
present. INLP is a popular recent method which removes specific information
through iterative nul... | true | true | Haghighatkhah, Pantea and
Fokkens, Antske and
Sommerauer, Pia and
Speckmann, Bettina and
Verbeek, Kevin | 2,022 | null | https://aclanthology.org/2022.emnlp-main.575 | 10.18653/v1/2022.emnlp-main.575 | null | Better Hit the Nail on the Head than Beat around the Bush: Removing
Protected Attributes with a Single Projection | Better Hit the Nail on the Head than Beat around the Bush | https://www.researchgate.net/publication/366135893_Better_Hit_the_Nail_on_the_Head_than_Beat_around_the_Bush_Removing_Protected_Attributes_with_a_Single_Projection | Our comparison between MP and INLP shows that (1) one MP projection removes linear separability based on the target and (2) MP has less impact |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | ravfogel2020nulloutguardingprotected | \cite{ravfogel2020nulloutguardingprotected} | Null It Out: Guarding Protected Attributes by Iterative Nullspace
Projection | http://arxiv.org/abs/2004.07667v2 | The ability to control for the kinds of information encoded in neural
representation has a variety of use cases, especially in light of the challenge
of interpreting these models. We present Iterative Null-space Projection
(INLP), a novel method for removing information from neural representations.
Our method is based ... | true | true | Ravfogel, Shauli and
Elazar, Yanai and
Gonen, Hila and
Twiton, Michael and
Goldberg, Yoav | 2,020 | null | https://aclanthology.org/2020.acl-main.647 | 10.18653/v1/2020.acl-main.647 | null | Null It Out: Guarding Protected Attributes by Iterative Nullspace
Projection | Shauli Ravfogel - Google Scholar | https://scholar.google.co.il/citations?user=x09r-T8AAAAJ&hl=en | Null it out: Guarding protected attributes by iterative nullspace projection. S Ravfogel, Y Elazar, H Gonen, M Twiton, Y Goldberg. Proceedings of the 58th |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | belrose2023leaceperfectlinearconcept | \cite{belrose2023leaceperfectlinearconcept} | LEACE: Perfect linear concept erasure in closed form | http://arxiv.org/abs/2306.03819v4 | Concept erasure aims to remove specified features from an embedding. It can
improve fairness (e.g. preventing a classifier from using gender or race) and
interpretability (e.g. removing a concept to observe changes in model
behavior). We introduce LEAst-squares Concept Erasure (LEACE), a closed-form
method which provab... | true | true | Nora Belrose and
David Schneider{-}Joseph and
Shauli Ravfogel and
Ryan Cotterell and
Edward Raff and
Stella Biderman | 2,023 | null | http://papers.nips.cc/paper\_files/paper/2023/hash/d066d21c619d0a78c5b557fa3291a8f4-Abstract-Conference.html | null | null | LEACE: Perfect linear concept erasure in closed form | LEACE: Perfect linear concept erasure in closed form | http://arxiv.org/pdf/2306.03819v4 | Concept erasure aims to remove specified features from an embedding. It can
improve fairness (e.g. preventing a classifier from using gender or race) and
interpretability (e.g. removing a concept to observe changes in model
behavior). We introduce LEAst-squares Concept Erasure (LEACE), a closed-form
method which provab... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | wang2024trojanactivationattackredteaming | \cite{wang2024trojanactivationattackredteaming} | Trojan Activation Attack: Red-Teaming Large Language Models using
Activation Steering for Safety-Alignment | http://arxiv.org/abs/2311.09433v3 | To ensure AI safety, instruction-tuned Large Language Models (LLMs) are
specifically trained to ensure alignment, which refers to making models behave
in accordance with human intentions. While these models have demonstrated
commendable results on various safety benchmarks, the vulnerability of their
safety alignment h... | true | true | Haoran Wang and Kai Shu | 2,023 | null | https://arxiv.org/abs/2311.09433 | null | ArXiv preprint | Trojan Activation Attack: Red-Teaming Large Language Models using
Activation Steering for Safety-Alignment | Trojan Activation Attack: Red-Teaming Large Language Models ... | https://arxiv.org/html/2311.09433v3 | Trojan Activation Attack: Red-Teaming Large Language Models using Activation Steering for Safety-Alignment Trojan Activation Attack: Red-Teaming Large Language Models using Activation Steering for Safety-Alignment Large Language Models (LLMs) are generally trained on massive text corpora scraped from the web (Touvron e... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | bolukbasi2016man | \cite{bolukbasi2016man} | Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word
Embeddings | http://arxiv.org/abs/1607.06520v1 | The blind application of machine learning runs the risk of amplifying biases
present in data. Such a danger is facing us with word embedding, a popular
framework to represent text data as vectors which has been used in many machine
learning and natural language processing tasks. We show that even word
embeddings traine... | true | true | Tolga Bolukbasi and
Kai{-}Wei Chang and
James Y. Zou and
Venkatesh Saligrama and
Adam Tauman Kalai | 2,016 | null | https://proceedings.neurips.cc/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html | null | null | Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word
Embeddings | Tolga Bolukbasi - Google Scholar | https://scholar.google.com/citations?user=3rF9gtAAAAAJ&hl=en | Man is to Computer Programmer as Woman is to Homemaker. T Bolukbasi, KW Chang, J Zou, V Saligrama, A Kalai. Debiasing word embeddings 29, 2016. 240, 2016. |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | elhage2022toymodelssuperposition | \cite{elhage2022toymodelssuperposition} | Toy Models of Superposition | http://arxiv.org/abs/2209.10652v1 | Neural networks often pack many unrelated concepts into a single neuron - a
puzzling phenomenon known as 'polysemanticity' which makes interpretability
much more challenging. This paper provides a toy model where polysemanticity
can be fully understood, arising as a result of models storing additional
sparse features i... | true | true | Nelson Elhage and Tristan Hume and Catherine Olsson and Nicholas Schiefer and Tom Henighan and Shauna Kravec and Zac Hatfield-Dodds and Robert Lasenby and Dawn Drain and Carol Chen and Roger Grosse and Sam McCandlish and Jared Kaplan and Dario Amodei and Martin Wattenberg and Christopher Olah | 2,022 | null | https://arxiv.org/abs/2209.10652 | null | ArXiv preprint | Toy Models of Superposition | Toy Models of Superposition | http://arxiv.org/pdf/2209.10652v1 | Neural networks often pack many unrelated concepts into a single neuron - a
puzzling phenomenon known as 'polysemanticity' which makes interpretability
much more challenging. This paper provides a toy model where polysemanticity
can be fully understood, arising as a result of models storing additional
sparse features i... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | park2024linearrepresentationhypothesisgeometry | \cite{park2024linearrepresentationhypothesisgeometry} | The Linear Representation Hypothesis and the Geometry of Large Language
Models | http://arxiv.org/abs/2311.03658v2 | Informally, the 'linear representation hypothesis' is the idea that
high-level concepts are represented linearly as directions in some
representation space. In this paper, we address two closely related questions:
What does "linear representation" actually mean? And, how do we make sense of
geometric notions (e.g., cos... | true | true | Kiho Park and
Yo Joong Choe and
Victor Veitch | 2,024 | null | https://openreview.net/forum?id=UGpGkLzwpP | null | null | The Linear Representation Hypothesis and the Geometry of Large Language
Models | NeurIPS The Linear Representation Hypothesis in Language Models | https://neurips.cc/virtual/2023/77537 | In the context of large language models, the "linear representation hypothesis" is the idea that high-level concepts are represented linearly as directions |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | mikolov2013linguistic | \cite{mikolov2013linguistic} | Linguistic Regularities in Continuous Space Word Representations | null | null | true | false | Mikolov, Tomas and
Yih, Wen-tau and
Zweig, Geoffrey | 2,013 | null | https://aclanthology.org/N13-1090 | null | null | Linguistic Regularities in Continuous Space Word Representations | arXiv:1806.07978v1 [cs.LG] 20 Jun 2018 | https://arxiv.org/pdf/1806.07978 | by T Eichinger · 2018 · Cited by 1 — Mikolov, W. Yih, and G. Zweig, “Linguistic regularities in continuous space word representations.” in HLT-NAACL, 2013, pp. 746– |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | nanda2023emergentlinearrepresentationsworld | \cite{nanda2023emergentlinearrepresentationsworld} | Emergent Linear Representations in World Models of Self-Supervised
Sequence Models | http://arxiv.org/abs/2309.00941v2 | How do sequence models represent their decision-making process? Prior work
suggests that Othello-playing neural network learned nonlinear models of the
board state (Li et al., 2023). In this work, we provide evidence of a closely
related linear representation of the board. In particular, we show that probing
for "my co... | true | true | Nanda, Neel and
Lee, Andrew and
Wattenberg, Martin | 2,023 | null | https://aclanthology.org/2023.blackboxnlp-1.2 | 10.18653/v1/2023.blackboxnlp-1.2 | null | Emergent Linear Representations in World Models of Self-Supervised
Sequence Models | Emergent Linear Representations in World Models of Self- ... | https://huggingface.co/papers/2309.00941 | Sequence models use linear representations to interpret their decision-making processes in games like Othello, allowing for control of model |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | hernandez2021lowdimensionallineargeometrycontextualized | \cite{hernandez2021lowdimensionallineargeometrycontextualized} | The Low-Dimensional Linear Geometry of Contextualized Word
Representations | http://arxiv.org/abs/2105.07109v2 | Black-box probing models can reliably extract linguistic features like tense,
number, and syntactic role from pretrained word representations. However, the
manner in which these features are encoded in representations remains poorly
understood. We present a systematic study of the linear geometry of
contextualized word... | true | true | Hernandez, Evan and
Andreas, Jacob | 2,021 | null | https://aclanthology.org/2021.conll-1.7 | 10.18653/v1/2021.conll-1.7 | null | The Low-Dimensional Linear Geometry of Contextualized Word
Representations | Evan Hernandez - Google Scholar | https://scholar.google.com/citations?user=38EC20cAAAAJ&hl=en | The low-dimensional linear geometry of contextualized word representations. E Hernandez, J Andreas. arXiv preprint arXiv:2105.07109, 2021. 50, 2021. A |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | bricken2023monosemanticity | \cite{bricken2023monosemanticity} | Towards Monosemanticity: Decomposing Language Models With Dictionary Learning | null | null | true | false | Bricken, Trenton and Templeton, Adly and Batson, Joshua and Chen, Brian and Jermyn, Adam and Conerly, Tom and Turner, Nick and Anil, Cem and Denison, Carson and Askell, Amanda and Lasenby, Robert and Wu, Yifan and Kravec, Shauna and Schiefer, Nicholas and Maxwell, Tim and Joseph, Nicholas and Hatfield-Dodds, Zac and Ta... | 2,023 | null | null | null | Transformer Circuits Thread | Towards Monosemanticity: Decomposing Language Models With Dictionary Learning | Decomposing Language Models With Dictionary Learning | https://www.anthropic.com/research/towards-monosemanticity-decomposing-language-models-with-dictionary-learning | In our latest paper, Towards Monosemanticity: Decomposing Language Models With Dictionary Learning, we outline evidence that there are better units of analysis |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | templeton2024scaling | \cite{templeton2024scaling} | Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet | null | null | true | false | Templeton, Adly and Conerly, Tom and Marcus, Jonathan and Lindsey, Jack and Bricken, Trenton and Chen, Brian and Pearce, Adam and Citro, Craig and Ameisen, Emmanuel and Jones, Andy and Cunningham, Hoagy and Turner, Nicholas L and McDougall, Callum and MacDiarmid, Monte and Freeman, C. Daniel and Sumers, Theodore R. and... | 2,024 | null | https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html | null | Transformer Circuits Thread | Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet | arXiv:2406.17969v2 [cs.CL] 15 Oct 2024 | https://arxiv.org/pdf/2406.17969 | by H Yan · 2024 · Cited by 8 — Scaling monosemanticity: Extracting interpretable · features from claude 3 sonnet. Transformer Circuits. Thread. Hugo Touvron, Thibaut Lavril |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | cunningham2023sparseautoencodershighlyinterpretable | \cite{cunningham2023sparseautoencodershighlyinterpretable} | Sparse Autoencoders Find Highly Interpretable Features in Language
Models | http://arxiv.org/abs/2309.08600v3 | One of the roadblocks to a better understanding of neural networks' internals
is \textit{polysemanticity}, where neurons appear to activate in multiple,
semantically distinct contexts. Polysemanticity prevents us from identifying
concise, human-understandable explanations for what neural networks are doing
internally. ... | true | true | Robert Huben and
Hoagy Cunningham and
Logan Riggs and
Aidan Ewart and
Lee Sharkey | 2,024 | null | https://openreview.net/forum?id=F76bwRSLeK | null | null | Sparse Autoencoders Find Highly Interpretable Features in Language
Models | Sparse Autoencoders Find Highly Interpretable Features in ... | https://openreview.net/forum?id=F76bwRSLeK | This paper proposes using sparse autoencoders to learn interpretable and monosemantic features from the internal activations of language models. This paper presents a way to make the individual features of Large Language Models more interpretable by learning simple autoencoders with activation sparsity. On the original... |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | pearce2024bilinearmlpsenableweightbased | \cite{pearce2024bilinearmlpsenableweightbased} | Bilinear MLPs enable weight-based mechanistic interpretability | http://arxiv.org/abs/2410.08417v2 | A mechanistic understanding of how MLPs do computation in deep neural
networks remains elusive. Current interpretability work can extract features
from hidden activations over an input dataset but generally cannot explain how
MLP weights construct features. One challenge is that element-wise
nonlinearities introduce hi... | true | true | Michael T. Pearce and Thomas Dooms and Alice Rigg and Jose M. Oramas and Lee Sharkey | 2,024 | null | https://arxiv.org/abs/2410.08417 | null | ArXiv preprint | Bilinear MLPs enable weight-based mechanistic interpretability | Bilinear MLPs enable weight-based mechanistic ... | https://openreview.net/forum?id=gI0kPklUKS | by MT Pearce · Cited by 2 — The close-to-linear structure of bilinear MLPs enables weight-based analysis that reveals interpretable low rank structure across multiple modalities. |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | elhage2021mathematical | \cite{elhage2021mathematical} | A Mathematical Framework for Transformer Circuits | null | null | true | false | Elhage, Nelson and Nanda, Neel and Olsson, Catherine and Henighan, Tom and Joseph, Nicholas and Mann, Ben and Askell, Amanda and Bai, Yuntao and Chen, Anna and Conerly, Tom and DasSarma, Nova and Drain, Dawn and Ganguli, Deep and Hatfield-Dodds, Zac and Hernandez, Danny and Jones, Andy and Kernion, Jackson and Lovitt, ... | 2,021 | null | null | null | Transformer Circuits Thread | A Mathematical Framework for Transformer Circuits | A Walkthrough of A Mathematical Framework for ... | https://www.neelnanda.io/mechanistic-interpretability/a-walkthrough-of-a-mathematical-framework-for-transformer-circuits | A Mathematical Framework for Transformer Circuits is, in my opinion, the coolest paper I've ever had the privilege of working on. |
COSMIC: Generalized Refusal Direction Identification in LLM Activations | 2506.00085v1 | lieberum2023doescircuitanalysisinterpretability | \cite{lieberum2023doescircuitanalysisinterpretability} | Does Circuit Analysis Interpretability Scale? Evidence from Multiple
Choice Capabilities in Chinchilla | http://arxiv.org/abs/2307.09458v3 | \emph{Circuit analysis} is a promising technique for understanding the
internal mechanisms of language models. However, existing analyses are done in
small models far from the state of the art. To address this, we present a case
study of circuit analysis in the 70B Chinchilla model, aiming to test the
scalability of ci... | true | true | Tom Lieberum and Matthew Rahtz and János Kramár and Neel Nanda and Geoffrey Irving and Rohin Shah and Vladimir Mikulik | 2,023 | null | https://arxiv.org/abs/2307.09458 | null | ArXiv preprint | Does Circuit Analysis Interpretability Scale? Evidence from Multiple
Choice Capabilities in Chinchilla | Does Circuit Analysis Interpretability Scale? Evidence from Multiple ... | https://arxiv.org/abs/2307.09458 | Missing: 04/08/2025 |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | liang2022holistic | \cite{liang2022holistic} | Holistic Evaluation of Language Models | http://arxiv.org/abs/2211.09110v2 | Language models (LMs) are becoming the foundation for almost all major
language technologies, but their capabilities, limitations, and risks are not
well understood. We present Holistic Evaluation of Language Models (HELM) to
improve the transparency of language models. First, we taxonomize the vast
space of potential ... | true | true | Liang, Percy and Bommasani, Rishi and Lee, Tony and Tsipras, Dimitris and Soylu, Dilara and Yasunaga, Michihiro and Zhang, Yian and Narayanan, Deepak and Wu, Yuhuai and Kumar, Ananya and others | 2,022 | null | null | null | arXiv preprint arXiv:2211.09110 | Holistic Evaluation of Language Models | Holistic Evaluation of Language Models | http://arxiv.org/pdf/2211.09110v2 | Language models (LMs) are becoming the foundation for almost all major
language technologies, but their capabilities, limitations, and risks are not
well understood. We present Holistic Evaluation of Language Models (HELM) to
improve the transparency of language models. First, we taxonomize the vast
space of potential ... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | hendrycks2020measuring | \cite{hendrycks2020measuring} | Measuring Massive Multitask Language Understanding | http://arxiv.org/abs/2009.03300v3 | We propose a new test to measure a text model's multitask accuracy. The test
covers 57 tasks including elementary mathematics, US history, computer science,
law, and more. To attain high accuracy on this test, models must possess
extensive world knowledge and problem solving ability. We find that while most
recent mode... | true | true | Hendrycks, Dan and Burns, Collin and Basart, Steven and Zou, Andy and Mazeika, Mantas and Song, Dawn and Steinhardt, Jacob | 2,021 | null | null | null | null | Measuring Massive Multitask Language Understanding | Measuring Massive Multitask Language Understanding | http://arxiv.org/pdf/2009.03300v3 | We propose a new test to measure a text model's multitask accuracy. The test
covers 57 tasks including elementary mathematics, US history, computer science,
law, and more. To attain high accuracy on this test, models must possess
extensive world knowledge and problem solving ability. We find that while most
recent mode... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | open-llm-leaderboard-v2 | \cite{open-llm-leaderboard-v2} | Open LLM Leaderboard v2 | null | null | true | false | Clémentine Fourrier and Nathan Habib and Alina Lozovskaya and Konrad Szafer and Thomas Wolf | 2,024 | null | null | null | null | Open LLM Leaderboard v2 | Hugging Face Upgrades Open LLM Leaderboard v2 for ... - InfoQ | https://www.infoq.com/news/2024/10/open-llm-leaderboard-v2-launch/ | Scaling Large Language Model Serving Infrastructure at Meta/presentations/llm-meta/en/smallimage/ye-charlotte-qi-thumbnail-1747727365712.jpg) She explains how traditional product management principles remain crucial while highlighting the nuances of working with LLMs. Learn about prompt engineering, data-driven develop... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | blodgett-etal-2020-language | \cite{blodgett-etal-2020-language} | Language (Technology) is Power: A Critical Survey of "Bias" in NLP | http://arxiv.org/abs/2005.14050v2 | We survey 146 papers analyzing "bias" in NLP systems, finding that their
motivations are often vague, inconsistent, and lacking in normative reasoning,
despite the fact that analyzing "bias" is an inherently normative process. We
further find that these papers' proposed quantitative techniques for measuring
or mitigati... | true | true | Blodgett, Su Lin and
Barocas, Solon and
Daum{\'e} III, Hal and
Wallach, Hanna | 2,020 | null | null | null | null | Language (Technology) is Power: A Critical Survey of "Bias" in NLP | Language (Technology) is Power: A Critical Survey of "Bias" in NLP | http://arxiv.org/pdf/2005.14050v2 | We survey 146 papers analyzing "bias" in NLP systems, finding that their
motivations are often vague, inconsistent, and lacking in normative reasoning,
despite the fact that analyzing "bias" is an inherently normative process. We
further find that these papers' proposed quantitative techniques for measuring
or mitigati... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | yang2024assessing | \cite{yang2024assessing} | Assessing Adversarial Robustness of Large Language Models: An Empirical
Study | http://arxiv.org/abs/2405.02764v2 | Large Language Models (LLMs) have revolutionized natural language processing,
but their robustness against adversarial attacks remains a critical concern. We
presents a novel white-box style attack approach that exposes vulnerabilities
in leading open-source LLMs, including Llama, OPT, and T5. We assess the impact
of m... | true | true | Yang, Zeyu and Meng, Zhao and Zheng, Xiaochen and Wattenhofer, Roger | 2,024 | null | null | null | null | Assessing Adversarial Robustness of Large Language Models: An Empirical
Study | [PDF] Assessing Adversarial Robustness of Large Language Models | https://genai-evaluation-kdd2024.github.io/genai-evalution-kdd2024/assets/papers/GenAI_Evaluation_KDD2024_paper_24.pdf | In this paper, we present an extensive study of three leading open- source LLMs: Llama, OPT, and T5. We evaluate the robustness of various sizes |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | hartvigsen2022toxigen | \cite{hartvigsen2022toxigen} | ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and
Implicit Hate Speech Detection | http://arxiv.org/abs/2203.09509v4 | Toxic language detection systems often falsely flag text that contains
minority group mentions as toxic, as those groups are often the targets of
online hate. Such over-reliance on spurious correlations also causes systems to
struggle with detecting implicitly toxic language. To help mitigate these
issues, we create To... | true | true | Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece | 2,022 | null | null | null | null | ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and
Implicit Hate Speech Detection | ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial ... | https://www.researchgate.net/publication/361059047_ToxiGen_A_Large-Scale_Machine-Generated_Dataset_for_Adversarial_and_Implicit_Hate_Speech_Detection | Toxigen is a large-scale dataset featuring over 270K machine-generated toxic and benign statements about 13 minority groups, specifically designed to expose |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | magooda2023framework | \cite{magooda2023framework} | A Framework for Automated Measurement of Responsible AI Harms in
Generative AI Applications | http://arxiv.org/abs/2310.17750v1 | We present a framework for the automated measurement of responsible AI (RAI)
metrics for large language models (LLMs) and associated products and services.
Our framework for automatically measuring harms from LLMs builds on existing
technical and sociotechnical expertise and leverages the capabilities of
state-of-the-a... | true | true | Magooda, Ahmed and Helyar, Alec and Jackson, Kyle and Sullivan, David and Atalla, Chad and Sheng, Emily and Vann, Dan and Edgar, Richard and Palangi, Hamid and Lutz, Roman and others | 2,023 | null | null | null | arXiv preprint arXiv:2310.17750 | A Framework for Automated Measurement of Responsible AI Harms in
Generative AI Applications | A Framework for Automated Measurement of Responsible ... | https://www.microsoft.com/en-us/research/publication/a-framework-for-automated-measurement-of-responsible-ai-harms-in-generative-ai-applications/?locale=zh-cn | We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | li2023survey | \cite{li2023survey} | A Survey on Fairness in Large Language Models | http://arxiv.org/abs/2308.10149v2 | Large Language Models (LLMs) have shown powerful performance and development
prospects and are widely deployed in the real world. However, LLMs can capture
social biases from unprocessed training data and propagate the biases to
downstream tasks. Unfair LLM systems have undesirable social impacts and
potential harms. I... | true | true | Li, Yingji and Du, Mengnan and Song, Rui and Wang, Xin and Wang, Ying | 2,023 | null | null | null | arXiv preprint arXiv:2308.10149 | A Survey on Fairness in Large Language Models | A Survey on Fairness in Large Language Models | http://arxiv.org/pdf/2308.10149v2 | Large Language Models (LLMs) have shown powerful performance and development
prospects and are widely deployed in the real world. However, LLMs can capture
social biases from unprocessed training data and propagate the biases to
downstream tasks. Unfair LLM systems have undesirable social impacts and
potential harms. I... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | mackraz2024evaluating | \cite{mackraz2024evaluating} | Evaluating Gender Bias Transfer between Pre-trained and Prompt-Adapted
Language Models | http://arxiv.org/abs/2412.03537v1 | Large language models (LLMs) are increasingly being adapted to achieve
task-specificity for deployment in real-world decision systems. Several
previous works have investigated the bias transfer hypothesis (BTH) by studying
the effect of the fine-tuning adaptation strategy on model fairness to find
that fairness in pre-... | true | true | Mackraz, Natalie and Sivakumar, Nivedha and Khorshidi, Samira and Patel, Krishna and Theobald, Barry-John and Zappella, Luca and Apostoloff, Nicholas | 2,024 | null | null | null | arXiv preprint arXiv:2412.03537 | Evaluating Gender Bias Transfer between Pre-trained and Prompt-Adapted
Language Models | Evaluating Gender Bias Transfer between Pre-trained and Prompt ... | https://openreview.net/forum?id=HyN9POiYhN | The primary purpose of this research is to understand if intrinsic bias in pre-trained models can transfer to downstream tasks upon prompting, to gain |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | patel2024fairness | \cite{patel2024fairness} | Fairness Dynamics During Training | http://arxiv.org/abs/2506.01709v1 | We investigate fairness dynamics during Large Language Model (LLM) training
to enable the diagnoses of biases and mitigations through training
interventions like early stopping; we find that biases can emerge suddenly and
do not always follow common performance metrics. We introduce two new metrics
to evaluate fairness... | true | true | Patel, Krishna and Sivakumar, Nivedha and Theobald, Barry-John and Zappella, Luca and Apostoloff, Nicholas | null | null | null | null | Neurips Evaluating Evaluations: Examining Best Practices for Measuring Broader Impacts of Generative AI Workshop 2024 | Fairness Dynamics During Training | Fairness Dynamics During Training | http://arxiv.org/pdf/2506.01709v1 | We investigate fairness dynamics during Large Language Model (LLM) training
to enable the diagnoses of biases and mitigations through training
interventions like early stopping; we find that biases can emerge suddenly and
do not always follow common performance metrics. We introduce two new metrics
to evaluate fairness... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | laskar2023systematic | \cite{laskar2023systematic} | A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark
Datasets | http://arxiv.org/abs/2305.18486v4 | The development of large language models (LLMs) such as ChatGPT has brought a
lot of attention recently. However, their evaluation in the benchmark academic
datasets remains under-explored due to the difficulty of evaluating the
generative outputs produced by this model against the ground truth. In this
paper, we aim t... | true | true | Laskar, Md Tahmid Rahman and Bari, M Saiful and Rahman, Mizanur and Bhuiyan, Md Amran Hossen and Joty, Shafiq and Huang, Jimmy Xiangji | 2,023 | null | null | null | null | A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark
Datasets | A Systematic Study and Comprehensive Evaluation of ChatGPT on ... | https://arxiv.org/abs/2305.18486 | Image 2: arxiv logo>cs> arXiv:2305.18486 **arXiv:2305.18486** (cs) View a PDF of the paper titled A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets, by Md Tahmid Rahman Laskar and 5 other authors View a PDF of the paper titled A Systematic Study and Comprehensive Evaluation of ChatGPT o... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | chu2024fairness | \cite{chu2024fairness} | Fairness in Large Language Models: A Taxonomic Survey | http://arxiv.org/abs/2404.01349v2 | Large Language Models (LLMs) have demonstrated remarkable success across
various domains. However, despite their promising performance in numerous
real-world applications, most of these algorithms lack fairness considerations.
Consequently, they may lead to discriminatory outcomes against certain
communities, particula... | true | true | Chu, Zhibo and Wang, Zichong and Zhang, Wenbin | 2,024 | null | null | null | ACM SIGKDD explorations newsletter | Fairness in Large Language Models: A Taxonomic Survey | Fairness in Large Language Models: A Taxonomic Survey | http://arxiv.org/pdf/2404.01349v2 | Large Language Models (LLMs) have demonstrated remarkable success across
various domains. However, despite their promising performance in numerous
real-world applications, most of these algorithms lack fairness considerations.
Consequently, they may lead to discriminatory outcomes against certain
communities, particula... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | wang2024ceb | \cite{wang2024ceb} | CEB: Compositional Evaluation Benchmark for Fairness in Large Language
Models | http://arxiv.org/abs/2407.02408v2 | As Large Language Models (LLMs) are increasingly deployed to handle various
natural language processing (NLP) tasks, concerns regarding the potential
negative societal impacts of LLM-generated content have also arisen. To
evaluate the biases exhibited by LLMs, researchers have recently proposed a
variety of datasets. H... | true | true | Wang, Song and Wang, Peng and Zhou, Tong and Dong, Yushun and Tan, Zhen and Li, Jundong | 2,024 | null | null | null | arXiv preprint arXiv:2407.02408 | CEB: Compositional Evaluation Benchmark for Fairness in Large Language
Models | CEB: Compositional Evaluation Benchmark for Fairness in Large... | https://openreview.net/forum?id=IUmj2dw5se | Summary: This paper proposes a comprehensive benchmark for bias and fairness in large language models. The authors first propose a multi-layers taxonomy that |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | ye2024benchmarking | \cite{ye2024benchmarking} | Benchmarking LLMs via Uncertainty Quantification | http://arxiv.org/abs/2401.12794v3 | The proliferation of open-source Large Language Models (LLMs) from various
institutions has highlighted the urgent need for comprehensive evaluation
methods. However, current evaluation platforms, such as the widely recognized
HuggingFace open LLM leaderboard, neglect a crucial aspect -- uncertainty,
which is vital for... | true | true | Ye, Fanghua and Yang, Mingming and Pang, Jianhui and Wang, Longyue and Wong, Derek F and Yilmaz, Emine and Shi, Shuming and Tu, Zhaopeng | 2,024 | null | null | null | arXiv preprint arXiv:2401.12794 | Benchmarking LLMs via Uncertainty Quantification | Benchmarking LLMs via Uncertainty Quantification | http://arxiv.org/pdf/2401.12794v3 | The proliferation of open-source Large Language Models (LLMs) from various
institutions has highlighted the urgent need for comprehensive evaluation
methods. However, current evaluation platforms, such as the widely recognized
HuggingFace open LLM leaderboard, neglect a crucial aspect -- uncertainty,
which is vital for... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | fabris2022algorithmic | \cite{fabris2022algorithmic} | Algorithmic Fairness Datasets: the Story so Far | http://arxiv.org/abs/2202.01711v4 | Data-driven algorithms are studied in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing
community of researchers has been investigating the equity of existing
algorithms and proposing novel ones, advancing the understanding of risks and
opportunities of automa... | true | true | Fabris, Alessandro and Messina, Stefano and Silvello, Gianmaria and Susto, Gian Antonio | 2,022 | null | null | null | null | Algorithmic Fairness Datasets: the Story so Far | Algorithmic Fairness Datasets: the Story so Far | http://arxiv.org/pdf/2202.01711v4 | Data-driven algorithms are studied in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing
community of researchers has been investigating the equity of existing
algorithms and proposing novel ones, advancing the understanding of risks and
opportunities of automa... |
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