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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,402.05195 | $λ$-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion
Models by Leveraging CLIP Latent Space | ['Maitreya Patel', 'Sangmin Jung', 'Chitta Baral', 'Yezhou Yang'] | ['cs.CV', 'cs.CL'] | Despite the recent advances in personalized text-to-image (P-T2I) generative
models, it remains challenging to perform finetuning-free multi-subject-driven
T2I in a resource-efficient manner. Predominantly, contemporary approaches,
involving the training of Hypernetworks and Multimodal Large Language Models
(MLLMs), re... | 2024-02-07T19:07:10Z | Project page: https://eclipse-t2i.github.io/Lambda-ECLIPSE/ | null | null | λ-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent Space | ['Maitreya Patel', 'Sangmin Jung', 'Chitta Baral', 'Yezhou Yang'] | 2,024 | Trans. Mach. Learn. Res. | 35 | 56 | ['Computer Science'] |
2,402.05369 | Noise Contrastive Alignment of Language Models with Explicit Rewards | ['Huayu Chen', 'Guande He', 'Lifan Yuan', 'Ganqu Cui', 'Hang Su', 'Jun Zhu'] | ['cs.LG', 'cs.CL'] | User intentions are typically formalized as evaluation rewards to be
maximized when fine-tuning language models (LMs). Existing alignment methods,
such as Direct Preference Optimization (DPO), are mainly tailored for pairwise
preference data where rewards are implicitly defined rather than explicitly
given. In this pap... | 2024-02-08T02:58:47Z | NeurIPS 2024 | null | null | Noise Contrastive Alignment of Language Models with Explicit Rewards | ['Huayu Chen', 'Guande He', 'Lifan Yuan', 'Hang Su', 'Jun Zhu'] | 2,024 | Neural Information Processing Systems | 56 | 53 | ['Computer Science'] |
2,402.05406 | Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes | ['Lucio Dery', 'Steven Kolawole', 'Jean-François Kagy', 'Virginia Smith', 'Graham Neubig', 'Ameet Talwalkar'] | ['cs.LG', 'cs.CL'] | Structured pruning is a promising approach to create smaller, faster LLMs.
However, existing methods typically rely on backward passes, which can inflate
memory requirements and compute costs. In this work we introduce Bonsai, a
gradient-free structured pruning method that eliminates the need for
backpropagation, signi... | 2024-02-08T04:48:26Z | 19 pages, 6 fiigures, 16 tables | null | null | null | null | null | null | null | null | null |
2,402.05424 | Neural Circuit Diagrams: Robust Diagrams for the Communication,
Implementation, and Analysis of Deep Learning Architectures | ['Vincent Abbott'] | ['cs.LG'] | Diagrams matter. Unfortunately, the deep learning community has no standard
method for diagramming architectures. The current combination of linear algebra
notation and ad-hoc diagrams fails to offer the necessary precision to
understand architectures in all their detail. However, this detail is critical
for faithful i... | 2024-02-08T05:42:13Z | null | Transactions on Machine Learning Research (2024) | null | Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architectures | ['Vincent Abbott'] | 2,024 | Trans. Mach. Learn. Res. | 6 | 50 | ['Computer Science'] |
2,402.05457 | It's Never Too Late: Fusing Acoustic Information into Large Language
Models for Automatic Speech Recognition | ['Chen Chen', 'Ruizhe Li', 'Yuchen Hu', 'Sabato Marco Siniscalchi', 'Pin-Yu Chen', 'Ensiong Chng', 'Chao-Han Huck Yang'] | ['cs.CL', 'cs.AI', 'cs.MM', 'cs.SD', 'eess.AS'] | Recent studies have successfully shown that large language models (LLMs) can
be successfully used for generative error correction (GER) on top of the
automatic speech recognition (ASR) output. Specifically, an LLM is utilized to
carry out a direct mapping from the N-best hypotheses list generated by an ASR
system to th... | 2024-02-08T07:21:45Z | Accepted to ICLR 2024, 17 pages. This work will be open sourced under
MIT license | null | null | null | null | null | null | null | null | null |
2,402.05672 | Multilingual E5 Text Embeddings: A Technical Report | ['Liang Wang', 'Nan Yang', 'Xiaolong Huang', 'Linjun Yang', 'Rangan Majumder', 'Furu Wei'] | ['cs.CL', 'cs.IR'] | This technical report presents the training methodology and evaluation
results of the open-source multilingual E5 text embedding models, released in
mid-2023. Three embedding models of different sizes (small / base / large) are
provided, offering a balance between the inference efficiency and embedding
quality. The tra... | 2024-02-08T13:47:50Z | 6 pages | null | null | null | null | null | null | null | null | null |
2,402.05755 | Spirit LM: Interleaved Spoken and Written Language Model | ['Tu Anh Nguyen', 'Benjamin Muller', 'Bokai Yu', 'Marta R. Costa-jussa', 'Maha Elbayad', 'Sravya Popuri', 'Christophe Ropers', 'Paul-Ambroise Duquenne', 'Robin Algayres', 'Ruslan Mavlyutov', 'Itai Gat', 'Mary Williamson', 'Gabriel Synnaeve', 'Juan Pino', 'Benoit Sagot', 'Emmanuel Dupoux'] | ['cs.CL', 'cs.SD', 'eess.AS'] | We introduce Spirit LM, a foundation multimodal language model that freely
mixes text and speech. Our model is based on a 7B pretrained text language
model that we extend to the speech modality by continuously training it on text
and speech units. Speech and text sequences are concatenated as a single stream
of tokens,... | 2024-02-08T15:39:32Z | null | null | null | null | null | null | null | null | null | null |
2,402.05804 | InkSight: Offline-to-Online Handwriting Conversion by Teaching
Vision-Language Models to Read and Write | ['Blagoj Mitrevski', 'Arina Rak', 'Julian Schnitzler', 'Chengkun Li', 'Andrii Maksai', 'Jesse Berent', 'Claudiu Musat'] | ['cs.CV', 'cs.AI'] | Digital note-taking is gaining popularity, offering a durable, editable, and
easily indexable way of storing notes in a vectorized form, known as digital
ink. However, a substantial gap remains between this way of note-taking and
traditional pen-and-paper note-taking, a practice that is still favored by a
vast majority... | 2024-02-08T16:41:41Z | Accepted by Transactions on Machine Learning Research | null | null | null | null | null | null | null | null | null |
2,402.05856 | Structure-Informed Protein Language Model | ['Zuobai Zhang', 'Jiarui Lu', 'Vijil Chenthamarakshan', 'Aurélie Lozano', 'Payel Das', 'Jian Tang'] | ['q-bio.BM', 'cs.LG'] | Protein language models are a powerful tool for learning protein
representations through pre-training on vast protein sequence datasets.
However, traditional protein language models lack explicit structural
supervision, despite its relevance to protein function. To address this issue,
we introduce the integration of re... | 2024-02-07T09:32:35Z | null | null | null | null | null | null | null | null | null | null |
2,402.05892 | Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data | ['Shufan Li', 'Harkanwar Singh', 'Aditya Grover'] | ['cs.CV'] | In recent years, Transformers have become the de-facto architecture for
sequence modeling on text and a variety of multi-dimensional data, such as
images and video. However, the use of self-attention layers in a Transformer
incurs prohibitive compute and memory complexity that scales quadratically
w.r.t. the sequence l... | 2024-02-08T18:30:50Z | 24 pages, 7 figures | null | null | Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data | ['Shufan Li', 'Harkanwar Singh', 'Aditya Grover'] | 2,024 | European Conference on Computer Vision | 64 | 66 | ['Computer Science'] |
2,402.05904 | FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs | ['Eun Cheol Choi', 'Emilio Ferrara'] | ['cs.CL', 'cs.CY', 'cs.HC', 'cs.SI'] | Our society is facing rampant misinformation harming public health and trust.
To address the societal challenge, we introduce FACT-GPT, a system leveraging
Large Language Models (LLMs) to automate the claim matching stage of
fact-checking. FACT-GPT, trained on a synthetic dataset, identifies social
media content that a... | 2024-02-08T18:43:05Z | null | null | null | null | null | null | null | null | null | null |
2,402.0593 | WebLINX: Real-World Website Navigation with Multi-Turn Dialogue | ['Xing Han Lù', 'Zdeněk Kasner', 'Siva Reddy'] | ['cs.CL', 'cs.CV', 'cs.LG'] | We propose the problem of conversational web navigation, where a digital
agent controls a web browser and follows user instructions to solve real-world
tasks in a multi-turn dialogue fashion. To support this problem, we introduce
WEBLINX - a large-scale benchmark of 100K interactions across 2300 expert
demonstrations o... | 2024-02-08T18:58:02Z | null | null | 10.5555/3692070.3693410 | null | null | null | null | null | null | null |
2,402.06094 | Rethinking Data Selection for Supervised Fine-Tuning | ['Ming Shen'] | ['cs.CL'] | Although supervised finetuning (SFT) has emerged as an essential technique to
align large language models with humans, it is considered superficial, with
style learning being its nature. At the same time, recent works indicate the
importance of data selection for SFT, showing that finetuning with high-quality
and diver... | 2024-02-08T23:02:04Z | null | null | null | null | null | null | null | null | null | null |
2,402.06332 | InternLM-Math: Open Math Large Language Models Toward Verifiable
Reasoning | ['Huaiyuan Ying', 'Shuo Zhang', 'Linyang Li', 'Zhejian Zhou', 'Yunfan Shao', 'Zhaoye Fei', 'Yichuan Ma', 'Jiawei Hong', 'Kuikun Liu', 'Ziyi Wang', 'Yudong Wang', 'Zijian Wu', 'Shuaibin Li', 'Fengzhe Zhou', 'Hongwei Liu', 'Songyang Zhang', 'Wenwei Zhang', 'Hang Yan', 'Xipeng Qiu', 'Jiayu Wang', 'Kai Chen', 'Dahua Lin'] | ['cs.CL'] | The math abilities of large language models can represent their abstract
reasoning ability. In this paper, we introduce and open-source our math
reasoning LLMs InternLM-Math which is continue pre-trained from InternLM2. We
unify chain-of-thought reasoning, reward modeling, formal reasoning, data
augmentation, and code ... | 2024-02-09T11:22:08Z | null | null | null | null | null | null | null | null | null | null |
2,402.06363 | StruQ: Defending Against Prompt Injection with Structured Queries | ['Sizhe Chen', 'Julien Piet', 'Chawin Sitawarin', 'David Wagner'] | ['cs.CR'] | Recent advances in Large Language Models (LLMs) enable exciting
LLM-integrated applications, which perform text-based tasks by utilizing their
advanced language understanding capabilities. However, as LLMs have improved,
so have the attacks against them. Prompt injection attacks are an important
threat: they trick the ... | 2024-02-09T12:15:51Z | To appear at USENIX Security Symposium 2025. Key words: prompt
injection defense, LLM security, LLM-integrated applications | null | null | StruQ: Defending Against Prompt Injection with Structured Queries | ['Sizhe Chen', 'Julien Piet', 'Chawin Sitawarin', 'David Wagner'] | 2,024 | arXiv.org | 89 | 65 | ['Computer Science'] |
2,402.06475 | Large Language Models for Captioning and Retrieving Remote Sensing
Images | ['João Daniel Silva', 'João Magalhães', 'Devis Tuia', 'Bruno Martins'] | ['cs.CV'] | Image captioning and cross-modal retrieval are examples of tasks that involve
the joint analysis of visual and linguistic information. In connection to
remote sensing imagery, these tasks can help non-expert users in extracting
relevant Earth observation information for a variety of applications. Still,
despite some pr... | 2024-02-09T15:31:01Z | null | null | null | null | null | null | null | null | null | null |
2,402.06584 | G-SciEdBERT: A Contextualized LLM for Science Assessment Tasks in German | ['Ehsan Latif', 'Gyeong-Geon Lee', 'Knut Neumann', 'Tamara Kastorff', 'Xiaoming Zhai'] | ['cs.CL', 'cs.AI'] | The advancement of natural language processing has paved the way for
automated scoring systems in various languages, such as German (e.g., German
BERT [G-BERT]). Automatically scoring written responses to science questions in
German is a complex task and challenging for standard G-BERT as they lack
contextual knowledge... | 2024-02-09T18:05:03Z | Accepted by EDM and Submitted to JEDM | null | null | null | null | null | null | null | null | null |
2,402.06617 | FaBERT: Pre-training BERT on Persian Blogs | ['Mostafa Masumi', 'Seyed Soroush Majd', 'Mehrnoush Shamsfard', 'Hamid Beigy'] | ['cs.CL'] | We introduce FaBERT, a Persian BERT-base model pre-trained on the HmBlogs
corpus, encompassing both informal and formal Persian texts. FaBERT is designed
to excel in traditional Natural Language Understanding (NLU) tasks, addressing
the intricacies of diverse sentence structures and linguistic styles prevalent
in the P... | 2024-02-09T18:50:51Z | null | null | null | null | null | null | null | null | null | null |
2,402.06619 | Aya Dataset: An Open-Access Collection for Multilingual Instruction
Tuning | ['Shivalika Singh', 'Freddie Vargus', 'Daniel Dsouza', 'Börje F. Karlsson', 'Abinaya Mahendiran', 'Wei-Yin Ko', 'Herumb Shandilya', 'Jay Patel', 'Deividas Mataciunas', 'Laura OMahony', 'Mike Zhang', 'Ramith Hettiarachchi', 'Joseph Wilson', 'Marina Machado', 'Luisa Souza Moura', 'Dominik Krzemiński', 'Hakimeh Fadaei', '... | ['cs.CL', 'cs.AI'] | Datasets are foundational to many breakthroughs in modern artificial
intelligence. Many recent achievements in the space of natural language
processing (NLP) can be attributed to the finetuning of pre-trained models on a
diverse set of tasks that enables a large language model (LLM) to respond to
instructions. Instruct... | 2024-02-09T18:51:49Z | null | null | null | null | null | null | null | null | null | null |
2,402.06698 | FNSPID: A Comprehensive Financial News Dataset in Time Series | ['Zihan Dong', 'Xinyu Fan', 'Zhiyuan Peng'] | ['q-fin.ST'] | Financial market predictions utilize historical data to anticipate future
stock prices and market trends. Traditionally, these predictions have focused
on the statistical analysis of quantitative factors, such as stock prices,
trading volumes, inflation rates, and changes in industrial production. Recent
advancements i... | 2024-02-09T04:26:13Z | null | null | null | null | null | null | null | null | null | null |
2,402.06852 | ChemLLM: A Chemical Large Language Model | ['Di Zhang', 'Wei Liu', 'Qian Tan', 'Jingdan Chen', 'Hang Yan', 'Yuliang Yan', 'Jiatong Li', 'Weiran Huang', 'Xiangyu Yue', 'Wanli Ouyang', 'Dongzhan Zhou', 'Shufei Zhang', 'Mao Su', 'Han-Sen Zhong', 'Yuqiang Li'] | ['cs.AI', 'cs.CL'] | Large language models (LLMs) have made impressive progress in chemistry
applications. However, the community lacks an LLM specifically designed for
chemistry. The main challenges are two-fold: firstly, most chemical data and
scientific knowledge are stored in structured databases, which limits the
model's ability to su... | 2024-02-10T01:11:59Z | 9 pages, 5 figures | null | null | null | null | null | null | null | null | null |
2,402.06888 | Analysis of Self-Supervised Speech Models on Children's Speech and
Infant Vocalizations | ['Jialu Li', 'Mark Hasegawa-Johnson', 'Nancy L. McElwain'] | ['eess.AS', 'cs.SD'] | To understand why self-supervised learning (SSL) models have empirically
achieved strong performances on several speech-processing downstream tasks,
numerous studies have focused on analyzing the encoded information of the SSL
layer representations in adult speech. Limited work has investigated how
pre-training and fin... | 2024-02-10T05:20:50Z | Accepted to 2024 ICASSP Workshop of Self-supervision in Audio, Speech
and Beyond (SASB) | null | null | Analysis of Self-Supervised Speech Models on Children’s Speech and Infant Vocalizations | ['Jialu Li', 'M. Hasegawa-Johnson', 'Nancy L. McElwain'] | 2,024 | 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) | 3 | 36 | ['Engineering', 'Computer Science', 'Medicine'] |
2,402.06894 | GenTranslate: Large Language Models are Generative Multilingual Speech
and Machine Translators | ['Yuchen Hu', 'Chen Chen', 'Chao-Han Huck Yang', 'Ruizhe Li', 'Dong Zhang', 'Zhehuai Chen', 'Eng Siong Chng'] | ['cs.CL', 'cs.AI', 'cs.LG', 'cs.SD', 'eess.AS'] | Recent advances in large language models (LLMs) have stepped forward the
development of multilingual speech and machine translation by its reduced
representation errors and incorporated external knowledge. However, both
translation tasks typically utilize beam search decoding and top-1 hypothesis
selection for inferenc... | 2024-02-10T07:20:49Z | 18 pages, Accepted by ACL 2024. This work is open sourced at:
https://github.com/YUCHEN005/GenTranslate | null | null | GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators | ['Yuchen Hu', 'Chen Chen', 'Chao-Han Huck Yang', 'Ruizhe Li', 'Dong Zhang', 'Zhehuai Chen', 'E. Chng'] | 2,024 | Annual Meeting of the Association for Computational Linguistics | 21 | 66 | ['Computer Science', 'Engineering'] |
2,402.06994 | A Change Detection Reality Check | ['Isaac Corley', 'Caleb Robinson', 'Anthony Ortiz'] | ['cs.CV', 'cs.LG'] | In recent years, there has been an explosion of proposed change detection
deep learning architectures in the remote sensing literature. These approaches
claim to offer state-of-the-art performance on different standard benchmark
datasets. However, has the field truly made significant progress? In this paper
we perform ... | 2024-02-10T17:02:53Z | null | null | null | null | null | null | null | null | null | null |
2,402.07023 | Gemini Goes to Med School: Exploring the Capabilities of Multimodal
Large Language Models on Medical Challenge Problems & Hallucinations | ['Ankit Pal', 'Malaikannan Sankarasubbu'] | ['cs.CL', 'cs.AI', 'cs.CV', 'cs.HC', 'cs.LG'] | Large language models have the potential to be valuable in the healthcare
industry, but it's crucial to verify their safety and effectiveness through
rigorous evaluation. For this purpose, we comprehensively evaluated both
open-source LLMs and Google's new multimodal LLM called Gemini across Medical
reasoning, hallucin... | 2024-02-10T19:08:28Z | Preprint version, Under Review | null | null | null | null | null | null | null | null | null |
2,402.07148 | X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for
Large Language Models with Applications in Protein Mechanics and Molecular
Design | ['Eric L. Buehler', 'Markus J. Buehler'] | ['cond-mat.soft', 'cond-mat.dis-nn', 'cs.AI', 'cs.CL', 'cs.LG', 'q-bio.QM'] | We report a mixture of expert strategy to create fine-tuned large language
models using a deep layer-wise token-level approach based on low-rank
adaptation (LoRA). Starting with a set of pre-trained LoRA adapters, our gating
strategy uses the hidden states to dynamically mix adapted layers, allowing the
resulting X-LoR... | 2024-02-11T10:23:34Z | null | null | null | null | null | null | null | null | null | null |
2,402.07319 | ODIN: Disentangled Reward Mitigates Hacking in RLHF | ['Lichang Chen', 'Chen Zhu', 'Davit Soselia', 'Jiuhai Chen', 'Tianyi Zhou', 'Tom Goldstein', 'Heng Huang', 'Mohammad Shoeybi', 'Bryan Catanzaro'] | ['cs.LG', 'cs.AI', 'cs.CL'] | In this work, we study the issue of reward hacking on the response length, a
challenge emerging in Reinforcement Learning from Human Feedback (RLHF) on
LLMs. A well-formatted, verbose but less helpful response from the LLMs can
often deceive LLMs or even human evaluators to achieve high scores. The same
issue also hold... | 2024-02-11T22:40:12Z | null | null | null | ODIN: Disentangled Reward Mitigates Hacking in RLHF | ['Lichang Chen', 'Chen Zhu', 'Davit Soselia', 'Jiuhai Chen', 'Tianyi Zhou', 'Tom Goldstein', 'Heng Huang', 'M. Shoeybi', 'Bryan Catanzaro'] | 2,024 | International Conference on Machine Learning | 66 | 58 | ['Computer Science'] |
2,402.0744 | Benchmarking and Building Long-Context Retrieval Models with LoCo and
M2-BERT | ['Jon Saad-Falcon', 'Daniel Y. Fu', 'Simran Arora', 'Neel Guha', 'Christopher Ré'] | ['cs.IR', 'cs.LG'] | Retrieval pipelines-an integral component of many machine learning
systems-perform poorly in domains where documents are long (e.g., 10K tokens or
more) and where identifying the relevant document requires synthesizing
information across the entire text. Developing long-context retrieval encoders
suitable for these dom... | 2024-02-12T06:43:52Z | International Conference on Machine Learning (ICML) 2024 | null | null | Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT | ['Jon Saad-Falcon', 'Daniel Y. Fu', 'Simran Arora', 'Neel Guha', "Christopher R'e"] | 2,024 | International Conference on Machine Learning | 18 | 58 | ['Computer Science'] |
2,402.07596 | Sheet Music Transformer: End-To-End Optical Music Recognition Beyond
Monophonic Transcription | ['Antonio Ríos-Vila', 'Jorge Calvo-Zaragoza', 'Thierry Paquet'] | ['cs.CV', 'cs.SD', 'eess.AS'] | State-of-the-art end-to-end Optical Music Recognition (OMR) has, to date,
primarily been carried out using monophonic transcription techniques to handle
complex score layouts, such as polyphony, often by resorting to simplifications
or specific adaptations. Despite their efficacy, these approaches imply
challenges rela... | 2024-02-12T11:52:21Z | Submitted to the International Conference on Document Analysis and
Recognition 2024 | null | null | null | null | null | null | null | null | null |
2,402.07625 | Autonomous Data Selection with Zero-shot Generative Classifiers for
Mathematical Texts | ['Yifan Zhang', 'Yifan Luo', 'Yang Yuan', 'Andrew C Yao'] | ['cs.CL', 'cs.AI', 'cs.LG'] | We present Autonomous Data Selection (AutoDS), a method that leverages base
language models themselves as zero-shot "generative classifiers" to
automatically curate high-quality mathematical texts. Unlike prior approaches
that require human annotations or training a dedicated data filter, AutoDS
relies solely on a mode... | 2024-02-12T13:09:21Z | 22 pages, 9 figures | null | null | Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts | ['Yifan Zhang', 'Yifan Luo', 'Yang Yuan', 'A. C. Yao'] | 2,024 | null | 19 | 50 | ['Computer Science'] |
2,402.0763 | G-Retriever: Retrieval-Augmented Generation for Textual Graph
Understanding and Question Answering | ['Xiaoxin He', 'Yijun Tian', 'Yifei Sun', 'Nitesh V. Chawla', 'Thomas Laurent', 'Yann LeCun', 'Xavier Bresson', 'Bryan Hooi'] | ['cs.LG'] | Given a graph with textual attributes, we enable users to `chat with their
graph': that is, to ask questions about the graph using a conversational
interface. In response to a user's questions, our method provides textual
replies and highlights the relevant parts of the graph. While existing works
integrate large langu... | 2024-02-12T13:13:04Z | null | null | null | null | null | null | null | null | null | null |
2,402.07688 | CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation
for Evaluating LLMs in Cybersecurity Knowledge | ['Norbert Tihanyi', 'Mohamed Amine Ferrag', 'Ridhi Jain', 'Tamas Bisztray', 'Merouane Debbah'] | ['cs.AI', 'cs.CR'] | Large Language Models (LLMs) are increasingly used across various domains,
from software development to cyber threat intelligence. Understanding all the
different fields of cybersecurity, which includes topics such as cryptography,
reverse engineering, and risk assessment, poses a challenge even for human
experts. To a... | 2024-02-12T14:53:28Z | null | null | null | null | null | null | null | null | null | null |
2,402.07827 | Aya Model: An Instruction Finetuned Open-Access Multilingual Language
Model | ['Ahmet Üstün', 'Viraat Aryabumi', 'Zheng-Xin Yong', 'Wei-Yin Ko', "Daniel D'souza", 'Gbemileke Onilude', 'Neel Bhandari', 'Shivalika Singh', 'Hui-Lee Ooi', 'Amr Kayid', 'Freddie Vargus', 'Phil Blunsom', 'Shayne Longpre', 'Niklas Muennighoff', 'Marzieh Fadaee', 'Julia Kreutzer', 'Sara Hooker'] | ['cs.CL'] | Recent breakthroughs in large language models (LLMs) have centered around a
handful of data-rich languages. What does it take to broaden access to
breakthroughs beyond first-class citizen languages? Our work introduces Aya, a
massively multilingual generative language model that follows instructions in
101 languages of... | 2024-02-12T17:34:13Z | null | null | null | Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model | ['A. Ustun', 'Viraat Aryabumi', 'Zheng-Xin Yong', 'Wei-Yin Ko', "Daniel D'souza", 'Gbemileke Onilude', 'Neel Bhandari', 'Shivalika Singh', 'Hui-Lee Ooi', 'Amr Kayid', 'Freddie Vargus', 'Phil Blunsom', 'Shayne Longpre', 'Niklas Muennighoff', 'Marzieh Fadaee', 'Julia Kreutzer', 'Sara Hooker'] | 2,024 | Annual Meeting of the Association for Computational Linguistics | 231 | 158 | ['Computer Science'] |
2,402.07865 | Prismatic VLMs: Investigating the Design Space of Visually-Conditioned
Language Models | ['Siddharth Karamcheti', 'Suraj Nair', 'Ashwin Balakrishna', 'Percy Liang', 'Thomas Kollar', 'Dorsa Sadigh'] | ['cs.CV', 'cs.AI', 'cs.CL', 'cs.LG'] | Visually-conditioned language models (VLMs) have seen growing adoption in
applications such as visual dialogue, scene understanding, and robotic task
planning; adoption that has fueled a wealth of new models such as LLaVa,
InstructBLIP, and PaLI-3. Despite the volume of new releases, key design
decisions around image p... | 2024-02-12T18:21:14Z | Published at ICML 2024. 22 pages, 11 figures. Training code and
models: https://github.com/TRI-ML/prismatic-vlms. Evaluation code:
https://github.com/TRI-ML/vlm-evaluation | null | null | null | null | null | null | null | null | null |
2,402.07894 | MODIPHY: Multimodal Obscured Detection for IoT using PHantom
Convolution-Enabled Faster YOLO | ['Shubhabrata Mukherjee', 'Cory Beard', 'Zhu Li'] | ['cs.CV'] | Low-light conditions and occluded scenarios impede object detection in
real-world Internet of Things (IoT) applications like autonomous vehicles and
security systems. While advanced machine learning models strive for accuracy,
their computational demands clash with the limitations of resource-constrained
devices, hampe... | 2024-02-12T18:56:53Z | This paper has been accepted for publication at the IEEE
International Conference on Image Processing (ICIP) 2024 | null | null | null | null | null | null | null | null | null |
2,402.08183 | Pixel Sentence Representation Learning | ['Chenghao Xiao', 'Zhuoxu Huang', 'Danlu Chen', 'G Thomas Hudson', 'Yizhi Li', 'Haoran Duan', 'Chenghua Lin', 'Jie Fu', 'Jungong Han', 'Noura Al Moubayed'] | ['cs.CL', 'cs.CV'] | Pretrained language models are long known to be subpar in capturing sentence
and document-level semantics. Though heavily investigated, transferring
perturbation-based methods from unsupervised visual representation learning to
NLP remains an unsolved problem. This is largely due to the discreteness of
subword units br... | 2024-02-13T02:46:45Z | null | null | null | null | null | null | null | null | null | null |
2,402.08268 | World Model on Million-Length Video And Language With Blockwise
RingAttention | ['Hao Liu', 'Wilson Yan', 'Matei Zaharia', 'Pieter Abbeel'] | ['cs.LG'] | Enabling long-context understanding remains a key challenge in scaling
existing sequence models -- a crucial component in developing generally
intelligent models that can process and operate over long temporal horizons
that potentially consist of millions of tokens. In this paper, we aim to
address these challenges by ... | 2024-02-13T07:47:36Z | null | null | null | null | null | null | null | null | null | null |
2,402.08327 | PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers | ['Weizhe Lin', 'Jingbiao Mei', 'Jinghong Chen', 'Bill Byrne'] | ['cs.CL'] | Large Multimodal Models (LMMs) excel in natural language and visual
understanding but are challenged by exacting tasks such as Knowledge-based
Visual Question Answering (KB-VQA) which involve the retrieval of relevant
information from document collections to use in shaping answers to questions.
We present an extensive ... | 2024-02-13T09:47:07Z | ACL 2024; Project page: https://preflmr.github.io/ | null | null | null | null | null | null | null | null | null |
2,402.08666 | Improving Generalization in Semantic Parsing by Increasing Natural
Language Variation | ['Irina Saparina', 'Mirella Lapata'] | ['cs.CL'] | Text-to-SQL semantic parsing has made significant progress in recent years,
with various models demonstrating impressive performance on the challenging
Spider benchmark. However, it has also been shown that these models often
struggle to generalize even when faced with small perturbations of previously
(accurately) par... | 2024-02-13T18:48:23Z | EACL 2024 | null | null | Improving Generalization in Semantic Parsing by Increasing Natural Language Variation | ['Irina Saparina', 'Mirella Lapata'] | 2,024 | Conference of the European Chapter of the Association for Computational Linguistics | 2 | 43 | ['Computer Science'] |
2,402.08777 | DNABERT-S: Pioneering Species Differentiation with Species-Aware DNA
Embeddings | ['Zhihan Zhou', 'Weimin Wu', 'Harrison Ho', 'Jiayi Wang', 'Lizhen Shi', 'Ramana V Davuluri', 'Zhong Wang', 'Han Liu'] | ['q-bio.GN', 'cs.AI', 'cs.CE', 'cs.CL'] | We introduce DNABERT-S, a tailored genome model that develops species-aware
embeddings to naturally cluster and segregate DNA sequences of different
species in the embedding space. Differentiating species from genomic sequences
(i.e., DNA and RNA) is vital yet challenging, since many real-world species
remain uncharact... | 2024-02-13T20:21:29Z | null | null | null | null | null | null | null | null | null | null |
2,402.08875 | Advancing Human Action Recognition with Foundation Models trained on
Unlabeled Public Videos | ['Yang Qian', 'Yinan Sun', 'Ali Kargarandehkordi', 'Parnian Azizian', 'Onur Cezmi Mutlu', 'Saimourya Surabhi', 'Pingyi Chen', 'Zain Jabbar', 'Dennis Paul Wall', 'Peter Washington'] | ['cs.CV'] | The increasing variety and quantity of tagged multimedia content on a variety
of online platforms offer a unique opportunity to advance the field of human
action recognition. In this study, we utilize 283,582 unique, unlabeled TikTok
video clips, categorized into 386 hashtags, to train a domain-specific
foundation mode... | 2024-02-14T00:41:10Z | 10 pages | null | null | Advancing Human Action Recognition with Foundation Models trained on Unlabeled Public Videos | ['Yang Qian', 'Yinan Sun', 'A. Kargarandehkordi', 'Parnian Azizian', 'O. Mutlu', 'Saimourya Surabhi', 'Pingyi Chen', 'Zain Jabbar', 'Dennis P. Wall', 'Peter Washington'] | 2,024 | null | 1 | 41 | ['Computer Science'] |
2,402.09025 | SLEB: Streamlining LLMs through Redundancy Verification and Elimination
of Transformer Blocks | ['Jiwon Song', 'Kyungseok Oh', 'Taesu Kim', 'Hyungjun Kim', 'Yulhwa Kim', 'Jae-Joon Kim'] | ['cs.CL', 'cs.LG'] | Large language models (LLMs) have proven to be highly effective across
various natural language processing tasks. However, their large number of
parameters poses significant challenges for practical deployment. Pruning, a
technique aimed at reducing the size and complexity of LLMs, offers a potential
solution by removi... | 2024-02-14T09:01:13Z | ICML 2024 | null | null | null | null | null | null | null | null | null |
2,402.09099 | Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in
Large Models | ['Xiongye Xiao', 'Heng Ping', 'Chenyu Zhou', 'Defu Cao', 'Yaxing Li', 'Yi-Zhuo Zhou', 'Shixuan Li', 'Nikos Kanakaris', 'Paul Bogdan'] | ['cs.AI'] | In recent years, there has been increasing attention on the capabilities of
large models, particularly in handling complex tasks that small-scale models
are unable to perform. Notably, large language models (LLMs) have demonstrated
``intelligent'' abilities such as complex reasoning and abstract language
comprehension,... | 2024-02-14T11:20:09Z | ICLR 2025: https://openreview.net/forum?id=nt8gBX58Kh | null | null | null | null | null | null | null | null | null |
2,402.09151 | Chinese MentalBERT: Domain-Adaptive Pre-training on Social Media for
Chinese Mental Health Text Analysis | ['Wei Zhai', 'Hongzhi Qi', 'Qing Zhao', 'Jianqiang Li', 'Ziqi Wang', 'Han Wang', 'Bing Xiang Yang', 'Guanghui Fu'] | ['cs.CL', 'cs.LG'] | In the current environment, psychological issues are prevalent and
widespread, with social media serving as a key outlet for individuals to share
their feelings. This results in the generation of vast quantities of data
daily, where negative emotions have the potential to precipitate crisis
situations. There is a recog... | 2024-02-14T13:08:25Z | null | null | null | null | null | null | null | null | null | null |
2,402.09205 | Tell Me More! Towards Implicit User Intention Understanding of Language
Model Driven Agents | ['Cheng Qian', 'Bingxiang He', 'Zhong Zhuang', 'Jia Deng', 'Yujia Qin', 'Xin Cong', 'Zhong Zhang', 'Jie Zhou', 'Yankai Lin', 'Zhiyuan Liu', 'Maosong Sun'] | ['cs.CL', 'cs.AI', 'cs.HC'] | Current language model-driven agents often lack mechanisms for effective user
participation, which is crucial given the vagueness commonly found in user
instructions. Although adept at devising strategies and performing tasks, these
agents struggle with seeking clarification and grasping precise user
intentions. To bri... | 2024-02-14T14:36:30Z | 26 pages, 5 tables, 6 figures | null | null | null | null | null | null | null | null | null |
2,402.09353 | DoRA: Weight-Decomposed Low-Rank Adaptation | ['Shih-Yang Liu', 'Chien-Yi Wang', 'Hongxu Yin', 'Pavlo Molchanov', 'Yu-Chiang Frank Wang', 'Kwang-Ting Cheng', 'Min-Hung Chen'] | ['cs.CL', 'cs.CV'] | Among the widely used parameter-efficient fine-tuning (PEFT) methods, LoRA
and its variants have gained considerable popularity because of avoiding
additional inference costs. However, there still often exists an accuracy gap
between these methods and full fine-tuning (FT). In this work, we first
introduce a novel weig... | 2024-02-14T17:59:34Z | ICML2024(Oral) | null | null | DoRA: Weight-Decomposed Low-Rank Adaptation | ['Shih-Yang Liu', 'Chien-Yi Wang', 'Hongxu Yin', 'Pavlo Molchanov', 'Yu-Chiang Frank Wang', 'Kwang-Ting Cheng', 'Min-Hung Chen'] | 2,024 | International Conference on Machine Learning | 423 | 59 | ['Computer Science'] |
2,402.09371 | Transformers Can Achieve Length Generalization But Not Robustly | ['Yongchao Zhou', 'Uri Alon', 'Xinyun Chen', 'Xuezhi Wang', 'Rishabh Agarwal', 'Denny Zhou'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Length generalization, defined as the ability to extrapolate from shorter
training sequences to longer test ones, is a significant challenge for language
models. This issue persists even with large-scale Transformers handling
relatively straightforward tasks. In this paper, we test the Transformer's
ability of length g... | 2024-02-14T18:18:29Z | null | null | null | null | null | null | null | null | null | null |
2,402.09391 | LlaSMol: Advancing Large Language Models for Chemistry with a
Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset | ['Botao Yu', 'Frazier N. Baker', 'Ziqi Chen', 'Xia Ning', 'Huan Sun'] | ['cs.AI', 'cs.CE', 'cs.CL'] | Chemistry plays a crucial role in many domains, such as drug discovery and
material science. While large language models (LLMs) such as GPT-4 exhibit
remarkable capabilities on natural language processing tasks, existing research
indicates that their performance on chemistry tasks is discouragingly low. In
this paper, ... | 2024-02-14T18:42:25Z | Accepted by COLM 2024 | null | null | LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset | ['Botao Yu', 'Frazier N. Baker', 'Ziqi Chen', 'Xia Ning', 'Huan Sun'] | 2,024 | arXiv.org | 51 | 82 | ['Computer Science'] |
2,402.09739 | QuRating: Selecting High-Quality Data for Training Language Models | ['Alexander Wettig', 'Aatmik Gupta', 'Saumya Malik', 'Danqi Chen'] | ['cs.CL', 'cs.LG'] | Selecting high-quality pre-training data is important for creating capable
language models, but existing methods rely on simple heuristics. We introduce
QuRating, a method for selecting pre-training data that can capture human
intuitions about data quality. In this paper, we investigate four qualities -
writing style, ... | 2024-02-15T06:36:07Z | Accepted at ICML 2024. The results for top-k selection have been
corrected. The code, models and data are available at
https://github.com/princeton-nlp/QuRating | null | null | QuRating: Selecting High-Quality Data for Training Language Models | ['Alexander Wettig', 'Aatmik Gupta', 'Saumya Malik', 'Danqi Chen'] | 2,024 | International Conference on Machine Learning | 81 | 0 | ['Computer Science'] |
2,402.09759 | Efficient Language Adaptive Pre-training: Extending State-of-the-Art
Large Language Models for Polish | ['Szymon Ruciński'] | ['cs.CL', 'cs.AI'] | This study explores the potential of fine-tuning foundational English Large
Language Models (LLMs) for generating Polish text. The first step involves
Language Adaptive Pre-training (LAPT) on a high-quality dataset of 3.11 GB,
consisting of 276 million Polish tokens. The LAPT is followed by additional
fine-tuning aimed... | 2024-02-15T07:17:10Z | 10 pages | null | null | null | null | null | null | null | null | null |
2,402.09844 | Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent | ['Quentin Gallouédec', 'Edward Beeching', 'Clément Romac', 'Emmanuel Dellandréa'] | ['cs.AI'] | The search for a general model that can operate seamlessly across multiple
domains remains a key goal in machine learning research. The prevailing
methodology in Reinforcement Learning (RL) typically limits models to a single
task within a unimodal framework, a limitation that contrasts with the broader
vision of a ver... | 2024-02-15T10:01:55Z | null | 38th Workshop on Aligning Reinforcement Learning Experimentalists
and Theorists (ARLET 2024) | null | null | null | null | null | null | null | null |
2,402.09906 | Generative Representational Instruction Tuning | ['Niklas Muennighoff', 'Hongjin Su', 'Liang Wang', 'Nan Yang', 'Furu Wei', 'Tao Yu', 'Amanpreet Singh', 'Douwe Kiela'] | ['cs.CL', 'cs.AI', 'cs.LG'] | 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... | 2024-02-15T12:12:19Z | 67 pages (16 main), 25 figures, 34 tables | null | null | null | null | null | null | null | null | null |
2,402.10176 | OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset | ['Shubham Toshniwal', 'Ivan Moshkov', 'Sean Narenthiran', 'Daria Gitman', 'Fei Jia', 'Igor Gitman'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Recent work has shown the immense potential of synthetically generated
datasets for training large language models (LLMs), especially for acquiring
targeted skills. Current large-scale math instruction tuning datasets such as
MetaMathQA (Yu et al., 2024) and MAmmoTH (Yue et al., 2024) are constructed
using outputs from... | 2024-02-15T18:26:11Z | Camera-ready version for NeurIPS 2024 | null | null | OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset | ['Shubham Toshniwal', 'Ivan Moshkov', 'Sean Narenthiran', 'Daria Gitman', 'Fei Jia', 'Igor Gitman'] | 2,024 | Neural Information Processing Systems | 97 | 40 | ['Computer Science'] |
2,402.10207 | Rewards-in-Context: Multi-objective Alignment of Foundation Models with
Dynamic Preference Adjustment | ['Rui Yang', 'Xiaoman Pan', 'Feng Luo', 'Shuang Qiu', 'Han Zhong', 'Dong Yu', 'Jianshu Chen'] | ['cs.LG', 'cs.AI', 'cs.CL'] | We consider the problem of multi-objective alignment of foundation models
with human preferences, which is a critical step towards helpful and harmless
AI systems. However, it is generally costly and unstable to fine-tune large
foundation models using reinforcement learning (RL), and the
multi-dimensionality, heterogen... | 2024-02-15T18:58:31Z | Accepted by ICML 2024 | null | null | Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment | ['Rui Yang', 'Xiaoman Pan', 'Feng Luo', 'Shuang Qiu', 'Han Zhong', 'Dong Yu', 'Jianshu Chen'] | 2,024 | International Conference on Machine Learning | 83 | 73 | ['Computer Science'] |
2,402.1021 | Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation | ['Huizhuo Yuan', 'Zixiang Chen', 'Kaixuan Ji', 'Quanquan Gu'] | ['cs.LG', 'cs.AI', 'cs.CL', 'cs.CV', 'stat.ML'] | Fine-tuning Diffusion Models remains an underexplored frontier in generative
artificial intelligence (GenAI), especially when compared with the remarkable
progress made in fine-tuning Large Language Models (LLMs). While cutting-edge
diffusion models such as Stable Diffusion (SD) and SDXL rely on supervised
fine-tuning,... | 2024-02-15T18:59:18Z | 28 pages, 8 figures, 10 tables | null | null | Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation | ['Huizhuo Yuan', 'Zixiang Chen', 'Kaixuan Ji', 'Quanquan Gu'] | 2,024 | Neural Information Processing Systems | 29 | 49 | ['Computer Science', 'Mathematics'] |
2,402.10373 | BioMistral: A Collection of Open-Source Pretrained Large Language Models
for Medical Domains | ['Yanis Labrak', 'Adrien Bazoge', 'Emmanuel Morin', 'Pierre-Antoine Gourraud', 'Mickael Rouvier', 'Richard Dufour'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Large Language Models (LLMs) have demonstrated remarkable versatility in
recent years, offering potential applications across specialized domains such
as healthcare and medicine. Despite the availability of various open-source
LLMs tailored for health contexts, adapting general-purpose LLMs to the medical
domain presen... | 2024-02-15T23:39:04Z | Accepted at ACL 2024 - Proceedings of the 62st Annual Meeting of the
Association for Computational Linguistics (Volume 1: Long Papers) | Proceedings of the 62st Annual Meeting of the Association for
Computational Linguistics - Volume 1: Long Papers (ACL 2024) | null | BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains | ['Yanis Labrak', 'Adrien Bazoge', 'Emmanuel Morin', 'P. Gourraud', 'Mickael Rouvier', 'Richard Dufour'] | 2,024 | Annual Meeting of the Association for Computational Linguistics | 228 | 69 | ['Computer Science'] |
2,402.10422 | Pushing the Limits of Zero-shot End-to-End Speech Translation | ['Ioannis Tsiamas', 'Gerard I. Gállego', 'José A. R. Fonollosa', 'Marta R. Costa-jussà'] | ['cs.CL'] | Data scarcity and the modality gap between the speech and text modalities are
two major obstacles of end-to-end Speech Translation (ST) systems, thus
hindering their performance. Prior work has attempted to mitigate these
challenges by leveraging external MT data and optimizing distance metrics that
bring closer the sp... | 2024-02-16T03:06:37Z | ACL 2024 (Findings) | null | null | null | null | null | null | null | null | null |
2,402.10453 | Steering Conversational Large Language Models for Long Emotional Support
Conversations | ['Navid Madani', 'Sougata Saha', 'Rohini Srihari'] | ['cs.CL'] | In this study, we address the challenge of enabling large language models
(LLMs) to consistently adhere to emotional support strategies in extended
conversations. We focus on the steerability of the Llama-2 and Llama-3 suite of
models, examining their ability to maintain these strategies throughout
interactions. To ass... | 2024-02-16T05:03:01Z | null | null | null | null | null | null | null | null | null | null |
2,402.10597 | Efficiency at Scale: Investigating the Performance of Diminutive
Language Models in Clinical Tasks | ['Niall Taylor', 'Upamanyu Ghose', 'Omid Rohanian', 'Mohammadmahdi Nouriborji', 'Andrey Kormilitzin', 'David Clifton', 'Alejo Nevado-Holgado'] | ['cs.CL', 'cs.AI'] | The entry of large language models (LLMs) into research and commercial spaces
has led to a trend of ever-larger models, with initial promises of
generalisability, followed by a widespread desire to downsize and create
specialised models without the need for complete fine-tuning, using Parameter
Efficient Fine-tuning (P... | 2024-02-16T11:30:11Z | null | null | null | null | null | null | null | null | null | null |
2,402.10631 | BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via
Self-Distillation | ['Dayou Du', 'Yijia Zhang', 'Shijie Cao', 'Jiaqi Guo', 'Ting Cao', 'Xiaowen Chu', 'Ningyi Xu'] | ['cs.CL'] | The upscaling of Large Language Models (LLMs) has yielded impressive advances
in natural language processing, yet it also poses significant deployment
challenges. Weight quantization has emerged as a widely embraced solution to
reduce memory and computational demands. This paper introduces BitDistiller, a
framework tha... | 2024-02-16T12:27:15Z | null | null | null | null | null | null | null | null | null | null |
2,402.10712 | An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient
Language Model Inference | ['Atsuki Yamaguchi', 'Aline Villavicencio', 'Nikolaos Aletras'] | ['cs.CL', 'cs.AI'] | The development of state-of-the-art generative large language models (LLMs)
disproportionately relies on English-centric tokenizers, vocabulary and
pre-training data. Despite the fact that some LLMs have multilingual
capabilities, recent studies have shown that their inference efficiency
deteriorates when generating te... | 2024-02-16T14:15:15Z | Accepted at EMNLP 2024 Findings | null | null | null | null | null | null | null | null | null |
2,402.10884 | Multi-modal Preference Alignment Remedies Degradation of Visual
Instruction Tuning on Language Models | ['Shengzhi Li', 'Rongyu Lin', 'Shichao Pei'] | ['cs.CL', 'cs.AI', 'cs.CV', 'cs.LG'] | Multi-modal large language models (MLLMs) are expected to support multi-turn
queries of interchanging image and text modalities in production. However, the
current MLLMs trained with visual-question-answering (VQA) datasets could
suffer from degradation, as VQA datasets lack the diversity and complexity of
the original... | 2024-02-16T18:42:08Z | Project code, model and data: https://github.com/findalexli/mllm-dpo | Proceedings of the 62nd Annual Meeting of the Association for
Computational Linguistics (Volume 1: Long Papers), pages 14188-14200, 2024 | 10.18653/v1/2024.acl-long.765 | null | null | null | null | null | null | null |
2,402.10886 | Reviewer2: Optimizing Review Generation Through Prompt Generation | ['Zhaolin Gao', 'Kianté Brantley', 'Thorsten Joachims'] | ['cs.CL'] | Recent developments in LLMs offer new opportunities for assisting authors in
improving their work. In this paper, we envision a use case where authors can
receive LLM-generated reviews that uncover weak points in the current draft.
While initial methods for automated review generation already exist, these
methods tend ... | 2024-02-16T18:43:10Z | null | null | null | null | null | null | null | null | null | null |
2,402.11073 | AFaCTA: Assisting the Annotation of Factual Claim Detection with
Reliable LLM Annotators | ['Jingwei Ni', 'Minjing Shi', 'Dominik Stammbach', 'Mrinmaya Sachan', 'Elliott Ash', 'Markus Leippold'] | ['cs.CL', 'cs.AI'] | With the rise of generative AI, automated fact-checking methods to combat
misinformation are becoming more and more important. However, factual claim
detection, the first step in a fact-checking pipeline, suffers from two key
issues that limit its scalability and generalizability: (1) inconsistency in
definitions of th... | 2024-02-16T20:59:57Z | ACL2024 Main Conference | null | null | null | null | null | null | null | null | null |
2,402.11095 | GIM: Learning Generalizable Image Matcher From Internet Videos | ['Xuelun Shen', 'Zhipeng Cai', 'Wei Yin', 'Matthias Müller', 'Zijun Li', 'Kaixuan Wang', 'Xiaozhi Chen', 'Cheng Wang'] | ['cs.CV'] | Image matching is a fundamental computer vision problem. While learning-based
methods achieve state-of-the-art performance on existing benchmarks, they
generalize poorly to in-the-wild images. Such methods typically need to train
separate models for different scene types and are impractical when the scene
type is unkno... | 2024-02-16T21:48:17Z | Accepted to ICLR 2024 for spotlight presentation | null | null | GIM: Learning Generalizable Image Matcher From Internet Videos | ['Xuelun Shen', 'Zhipeng Cai', 'Wei Yin', 'Matthias Müller', 'Zijun Li', 'Kaixuan Wang', 'Xiaozhi Chen', 'Cheng Wang'] | 2,024 | International Conference on Learning Representations | 30 | 40 | ['Computer Science'] |
2,402.11111 | Language Models as Science Tutors | ['Alexis Chevalier', 'Jiayi Geng', 'Alexander Wettig', 'Howard Chen', 'Sebastian Mizera', 'Toni Annala', 'Max Jameson Aragon', 'Arturo Rodríguez Fanlo', 'Simon Frieder', 'Simon Machado', 'Akshara Prabhakar', 'Ellie Thieu', 'Jiachen T. Wang', 'Zirui Wang', 'Xindi Wu', 'Mengzhou Xia', 'Wenhan Xia', 'Jiatong Yu', 'Jun-Jie... | ['cs.CL'] | NLP has recently made exciting progress toward training language models (LMs)
with strong scientific problem-solving skills. However, model development has
not focused on real-life use-cases of LMs for science, including applications
in education that require processing long scientific documents. To address
this, we in... | 2024-02-16T22:24:13Z | 8 pages without bibliography and appendix, 26 pages total | null | null | null | null | null | null | null | null | null |
2,402.11161 | PEDANTS: Cheap but Effective and Interpretable Answer Equivalence | ['Zongxia Li', 'Ishani Mondal', 'Yijun Liang', 'Huy Nghiem', 'Jordan Lee Boyd-Graber'] | ['cs.CL', 'cs.AI'] | Question answering (QA) can only make progress if we know if an answer is
correct, but current answer correctness (AC) metrics struggle with verbose,
free-form answers from large language models (LLMs). There are two challenges
with current short-form QA evaluations: a lack of diverse styles of evaluation
data and an o... | 2024-02-17T01:56:19Z | Efficient PEDANTS Classifier for short-form QA in github:
https://github.com/zli12321/qa_metrics. arXiv admin note: text overlap with
arXiv:2401.13170 | Empirical Methods in Natural Language Processing 2024 | null | PEDANTS: Cheap but Effective and Interpretable Answer Equivalence | ['Zongxia Li', 'Ishani Mondal', 'Huy Nghiem', 'Yijun Liang', 'Jordan L. Boyd-Graber'] | 2,024 | Conference on Empirical Methods in Natural Language Processing | 21 | 57 | ['Computer Science'] |
2,402.11176 | KnowTuning: Knowledge-aware Fine-tuning for Large Language Models | ['Yougang Lyu', 'Lingyong Yan', 'Shuaiqiang Wang', 'Haibo Shi', 'Dawei Yin', 'Pengjie Ren', 'Zhumin Chen', 'Maarten de Rijke', 'Zhaochun Ren'] | ['cs.CL', 'cs.AI'] | Despite their success at many natural language processing (NLP) tasks, large
language models still struggle to effectively leverage knowledge for
knowledge-intensive tasks, manifesting limitations such as generating
incomplete, non-factual, or illogical answers. These limitations stem from
inadequate knowledge awarenes... | 2024-02-17T02:54:32Z | EMNLP 2024 main paper | null | null | KnowTuning: Knowledge-aware Fine-tuning for Large Language Models | ['Yougang Lyu', 'Lingyong Yan', 'Shuaiqiang Wang', 'Haibo Shi', 'Dawei Yin', 'Pengjie Ren', 'Zhumin Chen', 'M. D. Rijke', 'Zhaochun Ren'] | 2,024 | Conference on Empirical Methods in Natural Language Processing | 7 | 113 | ['Computer Science'] |
2,402.11187 | LaCo: Large Language Model Pruning via Layer Collapse | ['Yifei Yang', 'Zouying Cao', 'Hai Zhao'] | ['cs.CL', 'cs.AI'] | Large language models (LLMs) based on transformer are witnessing a notable
trend of size expansion, which brings considerable costs to both model training
and inference. However, existing methods such as model quantization, knowledge
distillation, and model pruning are constrained by various issues, including
hardware ... | 2024-02-17T04:16:30Z | Accepted as Findings of EMNLP2024 | null | null | LaCo: Large Language Model Pruning via Layer Collapse | ['Yifei Yang', 'Zouying Cao', 'Hai Zhao'] | 2,024 | Conference on Empirical Methods in Natural Language Processing | 64 | 42 | ['Computer Science'] |
2,402.11248 | CoLLaVO: Crayon Large Language and Vision mOdel | ['Byung-Kwan Lee', 'Beomchan Park', 'Chae Won Kim', 'Yong Man Ro'] | ['cs.CV'] | The remarkable success of Large Language Models (LLMs) and instruction tuning
drives the evolution of Vision Language Models (VLMs) towards a versatile
general-purpose model. Yet, it remains unexplored whether current VLMs
genuinely possess quality object-level image understanding capabilities
determined from 'what obj... | 2024-02-17T11:03:02Z | ACL 2024 Findings. Code available:
https://github.com/ByungKwanLee/CoLLaVO | null | null | null | null | null | null | null | null | null |
2,402.11337 | Learning by Reconstruction Produces Uninformative Features For
Perception | ['Randall Balestriero', 'Yann LeCun'] | ['cs.CV', 'cs.AI', 'stat.ML'] | Input space reconstruction is an attractive representation learning paradigm.
Despite interpretability of the reconstruction and generation, we identify a
misalignment between learning by reconstruction, and learning for perception.
We show that the former allocates a model's capacity towards a subspace of the
data exp... | 2024-02-17T17:08:16Z | null | null | null | null | null | null | null | null | null | null |
2,402.11485 | LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models
with Entity-based Data Augmentation | ['Ikuya Yamada', 'Ryokan Ri'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Adapting English-based large language models (LLMs) to other languages has
become increasingly popular due to the efficiency and potential of
cross-lingual transfer. However, existing language adaptation methods often
overlook the benefits of cross-lingual supervision. In this study, we introduce
LEIA, a language adapt... | 2024-02-18T07:24:34Z | ACL Findings 2024 | null | null | null | null | null | null | null | null | null |
2,402.1153 | Efficient Multimodal Learning from Data-centric Perspective | ['Muyang He', 'Yexin Liu', 'Boya Wu', 'Jianhao Yuan', 'Yueze Wang', 'Tiejun Huang', 'Bo Zhao'] | ['cs.CV'] | Multimodal Large Language Models (MLLMs) have demonstrated notable
capabilities in general visual understanding and reasoning tasks. However,
their deployment is hindered by substantial computational costs in both
training and inference, limiting accessibility to the broader research and user
communities. A straightfor... | 2024-02-18T10:09:10Z | null | null | null | Efficient Multimodal Learning from Data-centric Perspective | ['Muyang He', 'Yexin Liu', 'Boya Wu', 'Jianhao Yuan', 'Yueze Wang', 'Tiejun Huang', 'Bo Zhao'] | 2,024 | arXiv.org | 88 | 63 | ['Computer Science'] |
2,402.11566 | Boosting Semi-Supervised 2D Human Pose Estimation by Revisiting Data
Augmentation and Consistency Training | ['Huayi Zhou', 'Mukun Luo', 'Fei Jiang', 'Yue Ding', 'Hongtao Lu', 'Kui Jia'] | ['cs.CV'] | The 2D human pose estimation (HPE) is a basic visual problem. However, its
supervised learning requires massive keypoint labels, which is labor-intensive
to collect. Thus, we aim at boosting a pose estimator by excavating extra
unlabeled data with semi-supervised learning (SSL). Most previous SSHPE methods
are consiste... | 2024-02-18T12:27:59Z | under review. Semi-Supervised 2D Human Pose Estimation | null | null | null | null | null | null | null | null | null |
2,402.11684 | ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language
Models | ['Guiming Hardy Chen', 'Shunian Chen', 'Ruifei Zhang', 'Junying Chen', 'Xiangbo Wu', 'Zhiyi Zhang', 'Zhihong Chen', 'Jianquan Li', 'Xiang Wan', 'Benyou Wang'] | ['cs.CL', 'cs.AI'] | Large vision-language models (LVLMs) have shown premise in a broad range of
vision-language tasks with their strong reasoning and generalization
capabilities. However, they require considerable computational resources for
training and deployment. This study aims to bridge the performance gap between
traditional-scale L... | 2024-02-18T19:26:49Z | 22 pages | null | null | ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models | ['Guiming Hardy Chen', 'Shunian Chen', 'Ruifei Zhang', 'Junying Chen', 'Xiangbo Wu', 'Zhiyi Zhang', 'Zhihong Chen', 'Jianquan Li', 'Xiang Wan', 'Benyou Wang'] | 2,024 | null | 139 | 44 | ['Computer Science'] |
2,402.11746 | Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned
Language Models through Task Arithmetic | ['Rishabh Bhardwaj', 'Do Duc Anh', 'Soujanya Poria'] | ['cs.CL', 'cs.AI'] | Aligned language models face a significant limitation as their fine-tuning
often results in compromised safety. To tackle this, we propose a simple method
RESTA that performs LLM safety realignment. RESTA stands for REstoring Safety
through Task Arithmetic. At its core, it involves a simple arithmetic addition
of a saf... | 2024-02-19T00:18:09Z | null | null | null | Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic | ['Rishabh Bhardwaj', 'Do Duc Anh', 'Soujanya Poria'] | 2,024 | Annual Meeting of the Association for Computational Linguistics | 48 | 49 | ['Computer Science'] |
2,402.11801 | Enhancing Empathetic Response Generation by Augmenting LLMs with
Small-scale Empathetic Models | ['Zhou Yang', 'Zhaochun Ren', 'Wang Yufeng', 'Shizhong Peng', 'Haizhou Sun', 'Xiaofei Zhu', 'Xiangwen Liao'] | ['cs.HC'] | Empathetic response generation is increasingly significant in AI,
necessitating nuanced emotional and cognitive understanding coupled with
articulate response expression. Current large language models (LLMs) excel in
response expression; however, they lack the ability to deeply understand
emotional and cognitive nuance... | 2024-02-19T03:12:12Z | 12 pages, 4 figures | null | null | null | null | null | null | null | null | null |
2,402.11809 | Generation Meets Verification: Accelerating Large Language Model
Inference with Smart Parallel Auto-Correct Decoding | ['Hanling Yi', 'Feng Lin', 'Hongbin Li', 'Peiyang Ning', 'Xiaotian Yu', 'Rong Xiao'] | ['cs.CL', 'cs.AI', 'cs.LG'] | This research aims to accelerate the inference speed of large language models
(LLMs) with billions of parameters. We propose \textbf{S}mart \textbf{P}arallel
\textbf{A}uto-\textbf{C}orrect d\textbf{E}coding (SPACE), an innovative
approach designed for achieving lossless acceleration of LLMs. By integrating
semi-autoreg... | 2024-02-19T03:39:10Z | Accepted by ACL 2024 Findings | null | null | null | null | null | null | null | null | null |
2,402.11811 | FIPO: Free-form Instruction-oriented Prompt Optimization with Preference
Dataset and Modular Fine-tuning Schema | ['Junru Lu', 'Siyu An', 'Min Zhang', 'Yulan He', 'Di Yin', 'Xing Sun'] | ['cs.CL'] | When the quality of naive prompts is carefully optimized by human experts,
the task performance of large language models (LLMs) can be significantly
improved. However, expert-based prompt optimizations are expensive. Herein,
some works have proposed Automatic Prompt Optimization (APO), to optimize naive
prompts accordi... | 2024-02-19T03:56:44Z | COLING 2025, Final Version | null | null | FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema | ['Junru Lu', 'Siyu An', 'Min Zhang', 'Yulan He', 'Di Yin', 'Xing Sun'] | 2,024 | International Conference on Computational Linguistics | 2 | 91 | ['Computer Science'] |
2,402.11819 | Head-wise Shareable Attention for Large Language Models | ['Zouying Cao', 'Yifei Yang', 'Hai Zhao'] | ['cs.CL'] | Large Language Models (LLMs) suffer from huge number of parameters, which
restricts their deployment on edge devices. Weight sharing is one promising
solution that encourages weight reuse, effectively reducing memory usage with
less performance drop. However, current weight sharing techniques primarily
focus on small-s... | 2024-02-19T04:19:36Z | 17 pages, 7 figures, 21 tables, EMNLP'24 Findings | null | null | null | null | null | null | null | null | null |
2,402.11882 | NOTE: Notable generation Of patient Text summaries through Efficient
approach based on direct preference optimization | ['Imjin Ahn', 'Hansle Gwon', 'Young-Hak Kim', 'Tae Joon Jun', 'Sanghyun Park'] | ['cs.CV', 'J.3'] | The discharge summary is a one of critical documents in the patient journey,
encompassing all events experienced during hospitalization, including multiple
visits, medications, tests, surgery/procedures, and admissions/discharge.
Providing a summary of the patient's progress is crucial, as it significantly
influences f... | 2024-02-19T06:43:25Z | 13 pages, 3 figures, 5 tables | null | null | null | null | null | null | null | null | null |
2,402.11883 | InMD-X: Large Language Models for Internal Medicine Doctors | ['Hansle Gwon', 'Imjin Ahn', 'Hyoje Jung', 'Byeolhee Kim', 'Young-Hak Kim', 'Tae Joon Jun'] | ['cs.CV'] | In this paper, we introduce InMD-X, a collection of multiple large language
models specifically designed to cater to the unique characteristics and demands
of Internal Medicine Doctors (IMD). InMD-X represents a groundbreaking
development in natural language processing, offering a suite of language models
fine-tuned fo... | 2024-02-19T06:46:16Z | null | null | null | null | null | null | null | null | null | null |
2,402.11929 | DiLightNet: Fine-grained Lighting Control for Diffusion-based Image
Generation | ['Chong Zeng', 'Yue Dong', 'Pieter Peers', 'Youkang Kong', 'Hongzhi Wu', 'Xin Tong'] | ['cs.CV', 'cs.GR'] | This paper presents a novel method for exerting fine-grained lighting control
during text-driven diffusion-based image generation. While existing diffusion
models already have the ability to generate images under any lighting
condition, without additional guidance these models tend to correlate image
content and lighti... | 2024-02-19T08:17:21Z | Accepted to SIGGRAPH 2024. Project page:
https://dilightnet.github.io/ | ACM SIGGRAPH 2024 Conference Proceedings | 10.1145/3641519.3657396 | null | null | null | null | null | null | null |
2,402.11975 | Compress to Impress: Unleashing the Potential of Compressive Memory in
Real-World Long-Term Conversations | ['Nuo Chen', 'Hongguang Li', 'Juhua Huang', 'Baoyuan Wang', 'Jia Li'] | ['cs.CL'] | Existing retrieval-based methods have made significant strides in maintaining
long-term conversations. However, these approaches face challenges in memory
database management and accurate memory retrieval, hindering their efficacy in
dynamic, real-world interactions. This study introduces a novel framework,
COmpressive... | 2024-02-19T09:19:50Z | 17pages, 5 figures | null | null | Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations | ['Nuo Chen', 'Hongguang Li', 'Juhua Huang', 'Baoyuan Wang', 'Jia Li'] | 2,024 | arXiv.org | 11 | 46 | ['Computer Science'] |
2,402.12052 | Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When
and What to Retrieve for LLMs | ['Jiejun Tan', 'Zhicheng Dou', 'Yutao Zhu', 'Peidong Guo', 'Kun Fang', 'Ji-Rong Wen'] | ['cs.CL'] | The integration of large language models (LLMs) and search engines represents
a significant evolution in knowledge acquisition methodologies. However,
determining the knowledge that an LLM already possesses and the knowledge that
requires the help of a search engine remains an unresolved issue. Most existing
methods so... | 2024-02-19T11:11:08Z | Accepted by ACL 2024 main conference. Repo:
https://github.com/plageon/SlimPLM | null | null | Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs | ['Jiejun Tan', 'Zhicheng Dou', 'Yutao Zhu', 'Peidong Guo', 'Kun Fang', 'Ji-Rong Wen'] | 2,024 | Annual Meeting of the Association for Computational Linguistics | 30 | 77 | ['Computer Science'] |
2,402.12195 | Browse and Concentrate: Comprehending Multimodal Content via prior-LLM
Context Fusion | ['Ziyue Wang', 'Chi Chen', 'Yiqi Zhu', 'Fuwen Luo', 'Peng Li', 'Ming Yan', 'Ji Zhang', 'Fei Huang', 'Maosong Sun', 'Yang Liu'] | ['cs.CL'] | With the bloom of Large Language Models (LLMs), Multimodal Large Language
Models (MLLMs) that incorporate LLMs with pre-trained vision models have
recently demonstrated impressive performance across diverse vision-language
tasks. However, they fall short to comprehend context involving multiple
images. A primary reason... | 2024-02-19T14:59:07Z | 17 pages, 5 figures | null | null | null | null | null | null | null | null | null |
2,402.12204 | Enhancing Multilingual Capabilities of Large Language Models through
Self-Distillation from Resource-Rich Languages | ['Yuanchi Zhang', 'Yile Wang', 'Zijun Liu', 'Shuo Wang', 'Xiaolong Wang', 'Peng Li', 'Maosong Sun', 'Yang Liu'] | ['cs.CL'] | While large language models (LLMs) have been pre-trained on multilingual
corpora, their performance still lags behind in most languages compared to a
few resource-rich languages. One common approach to mitigate this issue is to
translate training data from resource-rich languages into other languages and
then continue ... | 2024-02-19T15:07:32Z | null | null | null | null | null | null | null | null | null | null |
2,402.12208 | Language-Codec: Bridging Discrete Codec Representations and Speech
Language Models | ['Shengpeng Ji', 'Minghui Fang', 'Jialong Zuo', 'Ziyue Jiang', 'Dingdong Wang', 'Hanting Wang', 'Hai Huang', 'Zhou Zhao'] | ['eess.AS', 'cs.SD'] | In recent years, large language models have achieved significant success in
generative tasks related to speech, audio, music, and other signal domains. A
crucial element of these models is the discrete acoustic codecs, which serve as
an intermediate representation replacing the mel-spectrogram. However, there
exist sev... | 2024-02-19T15:12:12Z | ACL 2025 Main | null | null | null | null | null | null | null | null | null |
2,402.12226 | AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling | ['Jun Zhan', 'Junqi Dai', 'Jiasheng Ye', 'Yunhua Zhou', 'Dong Zhang', 'Zhigeng Liu', 'Xin Zhang', 'Ruibin Yuan', 'Ge Zhang', 'Linyang Li', 'Hang Yan', 'Jie Fu', 'Tao Gui', 'Tianxiang Sun', 'Yugang Jiang', 'Xipeng Qiu'] | ['cs.CL', 'cs.AI', 'cs.CV', 'cs.LG'] | We introduce AnyGPT, an any-to-any multimodal language model that utilizes
discrete representations for the unified processing of various modalities,
including speech, text, images, and music. AnyGPT can be trained stably without
any alterations to the current large language model (LLM) architecture or
training paradig... | 2024-02-19T15:33:10Z | 28 pages, 16 figures, under review, work in progress | null | null | null | null | null | null | null | null | null |
2,402.12332 | Triple-Encoders: Representations That Fire Together, Wire Together | ['Justus-Jonas Erker', 'Florian Mai', 'Nils Reimers', 'Gerasimos Spanakis', 'Iryna Gurevych'] | ['cs.CL'] | Search-based dialog models typically re-encode the dialog history at every
turn, incurring high cost. Curved Contrastive Learning, a representation
learning method that encodes relative distances between utterances into the
embedding space via a bi-encoder, has recently shown promising results for
dialog modeling at fa... | 2024-02-19T18:06:02Z | accepted at ACL 2024 (main conference) | null | null | Triple-Encoders: Representations That Fire Together, Wire Together | ['Justus-Jonas Erker', 'Florian Mai', 'Nils Reimers', 'Gerasimos Spanakis', 'Iryna Gurevych'] | 2,024 | Annual Meeting of the Association for Computational Linguistics | 2 | 38 | ['Computer Science'] |
2,402.12336 | Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings
for Robust Large Vision-Language Models | ['Christian Schlarmann', 'Naman Deep Singh', 'Francesco Croce', 'Matthias Hein'] | ['cs.LG', 'cs.AI', 'cs.CV', 'stat.ML'] | Multi-modal foundation models like OpenFlamingo, LLaVA, and GPT-4 are
increasingly used for various real-world tasks. Prior work has shown that these
models are highly vulnerable to adversarial attacks on the vision modality.
These attacks can be leveraged to spread fake information or defraud users, and
thus pose a si... | 2024-02-19T18:09:48Z | ICML 2024 Oral | null | null | null | null | null | null | null | null | null |
2,402.12354 | LoRA+: Efficient Low Rank Adaptation of Large Models | ['Soufiane Hayou', 'Nikhil Ghosh', 'Bin Yu'] | ['cs.LG', 'cs.AI', 'cs.CL', 'stat.ML'] | In this paper, we show that Low Rank Adaptation (LoRA) as originally
introduced in Hu et al. (2021) leads to suboptimal finetuning of models with
large width (embedding dimension). This is due to the fact that adapter
matrices A and B in LoRA are updated with the same learning rate. Using scaling
arguments for large wi... | 2024-02-19T18:33:49Z | 27 pages | null | null | null | null | null | null | null | null | null |
2,402.12374 | Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding | ['Zhuoming Chen', 'Avner May', 'Ruslan Svirschevski', 'Yuhsun Huang', 'Max Ryabinin', 'Zhihao Jia', 'Beidi Chen'] | ['cs.CL'] | As the usage of large language models (LLMs) grows, performing efficient
inference with these models becomes increasingly important. While speculative
decoding has recently emerged as a promising direction for speeding up
inference, existing methods are limited in their ability to scale to larger
speculation budgets, a... | 2024-02-19T18:58:32Z | null | null | null | Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding | ['Zhuoming Chen', 'Avner May', 'Ruslan Svirschevski', 'Yuhsun Huang', 'Max Ryabinin', 'Zhihao Jia', 'Beidi Chen'] | 2,024 | arXiv.org | 52 | 48 | ['Computer Science'] |
2,402.12376 | FiT: Flexible Vision Transformer for Diffusion Model | ['Zeyu Lu', 'Zidong Wang', 'Di Huang', 'Chengyue Wu', 'Xihui Liu', 'Wanli Ouyang', 'Lei Bai'] | ['cs.CV'] | Nature is infinitely resolution-free. In the context of this reality,
existing diffusion models, such as Diffusion Transformers, often face
challenges when processing image resolutions outside of their trained domain.
To overcome this limitation, we present the Flexible Vision Transformer (FiT),
a transformer architect... | 2024-02-19T18:59:07Z | null | null | null | null | null | null | null | null | null | null |
2,402.12399 | Turn Waste into Worth: Rectifying Top-$k$ Router of MoE | ['Zhiyuan Zeng', 'Qipeng Guo', 'Zhaoye Fei', 'Zhangyue Yin', 'Yunhua Zhou', 'Linyang Li', 'Tianxiang Sun', 'Hang Yan', 'Dahua Lin', 'Xipeng Qiu'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Sparse Mixture of Experts (MoE) models are popular for training large
language models due to their computational efficiency. However, the commonly
used top-$k$ routing mechanism suffers from redundancy computation and memory
costs due to the unbalanced routing. Some experts are overflow, where the
exceeding tokens are ... | 2024-02-17T06:23:27Z | null | null | null | null | null | null | null | null | null | null |
2,402.12479 | In value-based deep reinforcement learning, a pruned network is a good
network | ['Johan Obando-Ceron', 'Aaron Courville', 'Pablo Samuel Castro'] | ['cs.LG', 'cs.AI'] | Recent work has shown that deep reinforcement learning agents have difficulty
in effectively using their network parameters. We leverage prior insights into
the advantages of sparse training techniques and demonstrate that gradual
magnitude pruning enables value-based agents to maximize parameter
effectiveness. This re... | 2024-02-19T19:34:07Z | null | null | null | null | null | null | null | null | null | null |
2,402.12652 | PDEformer: Towards a Foundation Model for One-Dimensional Partial
Differential Equations | ['Zhanhong Ye', 'Xiang Huang', 'Leheng Chen', 'Hongsheng Liu', 'Zidong Wang', 'Bin Dong'] | ['math.NA', 'cs.NA'] | This paper introduces PDEformer, a neural solver for partial differential
equations (PDEs) capable of simultaneously addressing various types of PDEs. We
propose to represent the PDE in the form of a computational graph, facilitating
the seamless integration of both symbolic and numerical information inherent in
a PDE.... | 2024-02-20T02:02:29Z | null | null | null | null | null | null | null | null | null | null |
2,402.12749 | Me LLaMA: Foundation Large Language Models for Medical Applications | ['Qianqian Xie', 'Qingyu Chen', 'Aokun Chen', 'Cheng Peng', 'Yan Hu', 'Fongci Lin', 'Xueqing Peng', 'Jimin Huang', 'Jeffrey Zhang', 'Vipina Keloth', 'Xinyu Zhou', 'Lingfei Qian', 'Huan He', 'Dennis Shung', 'Lucila Ohno-Machado', 'Yonghui Wu', 'Hua Xu', 'Jiang Bian'] | ['cs.CL', 'cs.AI'] | Recent advancements in large language models (LLMs) like ChatGPT and LLaMA
show promise in medical applications, yet challenges remain in medical language
comprehension. This study presents Me-LLaMA, a new medical LLM family based on
open-source LLaMA models, optimized for medical text analysis and diagnosis by
leverag... | 2024-02-20T06:37:31Z | 21 pages, 4 figures, 8 tables | null | null | Me LLaMA: Foundation Large Language Models for Medical Applications | ['Qianqian Xie', 'Qingyu Chen', 'Aokun Chen', 'C.A.I. Peng', 'Yan Hu', 'Fongci Lin', 'Xueqing Peng', 'Jimin Huang', 'Jeffrey Zhang', 'V. Keloth', 'Xinyu Zhou', 'Lingfei Qian', 'Huan He', 'Dennis Shung', 'Lucila Ohno-Machado', 'Yonghui Wu', 'Hua Xu', 'Jiang Bian'] | 2,024 | null | 4 | 42 | ['Computer Science'] |
2,402.1284 | ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic | ['Fajri Koto', 'Haonan Li', 'Sara Shatnawi', 'Jad Doughman', 'Abdelrahman Boda Sadallah', 'Aisha Alraeesi', 'Khalid Almubarak', 'Zaid Alyafeai', 'Neha Sengupta', 'Shady Shehata', 'Nizar Habash', 'Preslav Nakov', 'Timothy Baldwin'] | ['cs.CL'] | The focus of language model evaluation has transitioned towards reasoning and
knowledge-intensive tasks, driven by advancements in pretraining large models.
While state-of-the-art models are partially trained on large Arabic texts,
evaluating their performance in Arabic remains challenging due to the limited
availabili... | 2024-02-20T09:07:41Z | Findings of ACL 2024 | null | null | ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic | ['Fajri Koto', 'Haonan Li', 'Sara Shatnawi', 'Jad Doughman', 'A. Sadallah', 'A. Alraeesi', 'Khalid Almubarak', 'Zaid Alyafeai', 'Neha Sengupta', 'Shady Shehata', 'Nizar Habash', 'Preslav Nakov', 'Timothy Baldwin'] | 2,024 | Annual Meeting of the Association for Computational Linguistics | 44 | 58 | ['Computer Science'] |
2,402.13022 | SoMeLVLM: A Large Vision Language Model for Social Media Processing | ['Xinnong Zhang', 'Haoyu Kuang', 'Xinyi Mou', 'Hanjia Lyu', 'Kun Wu', 'Siming Chen', 'Jiebo Luo', 'Xuanjing Huang', 'Zhongyu Wei'] | ['cs.CL', 'cs.MM'] | The growth of social media, characterized by its multimodal nature, has led
to the emergence of diverse phenomena and challenges, which calls for an
effective approach to uniformly solve automated tasks. The powerful Large
Vision Language Models make it possible to handle a variety of tasks
simultaneously, but even wit... | 2024-02-20T14:02:45Z | null | null | 10.18653/v1/2024.findings-acl.140 | null | null | null | null | null | null | null |
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