id stringlengths 10 10 | title stringlengths 8 162 | summary stringlengths 228 1.92k | source stringlengths 31 31 | authors stringlengths 7 6.97k | categories stringlengths 5 107 | comment stringlengths 4 398 ⌀ | journal_ref stringlengths 8 194 ⌀ | primary_category stringlengths 5 17 | published stringlengths 8 8 | updated stringlengths 8 8 | content stringlengths 3.91k 873k | references dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
2401.04088 | Mixtral of Experts | We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model.
Mixtral has the same architecture as Mistral 7B, with the difference that each
layer is composed of 8 feedforward blocks (i.e. experts). For every token, at
each layer, a router network selects two experts to process the current state
and com... | http://arxiv.org/pdf/2401.04088 | Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian... | cs.LG, cs.CL | See more details at https://mistral.ai/news/mixtral-of-experts/ | null | cs.LG | 20240108 | 20240108 | 4 2 0 2
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# Mixtral of Experts
Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lél... | {
"id": "1905.07830"
} |
2312.17238 | Fast Inference of Mixture-of-Experts Language Models with Offloading | With the widespread adoption of Large Language Models (LLMs), many deep
learning practitioners are looking for strategies of running these models more
efficiently. One such strategy is to use sparse Mixture-of-Experts (MoE) - a
type of model architectures where only a fraction of model layers are active
for any given i... | http://arxiv.org/pdf/2312.17238 | Artyom Eliseev, Denis Mazur | cs.LG, cs.AI, cs.DC | Technical report | null | cs.LG | 20231228 | 20231228 | 3 2 0 2
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# Fast Inference of Mixture-of-Experts Language Models with Offloading
Artyom Eliseev Moscow Institute of Physics and Technology Yandex School of Data Analysis lavawolfiee@gmail.com
# Denis Mazur Moscow Institute of Physics and Technology Yandex Resear... | {
"id": "2302.13971"
} |
2312.11111 | The Good, The Bad, and Why: Unveiling Emotions in Generative AI | Emotion significantly impacts our daily behaviors and interactions. While
recent generative AI models, such as large language models, have shown
impressive performance in various tasks, it remains unclear whether they truly
comprehend emotions. This paper aims to address this gap by incorporating
psychological theories... | http://arxiv.org/pdf/2312.11111 | Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Xinyi Wang, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, Xing Xie | cs.AI, cs.CL, cs.HC | Technical report; an extension to EmotionPrompt (arXiv:2307.11760);
34 pages | null | cs.AI | 20231218 | 20231219 | 3 2 0 2 c e D 9 1
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# The Good, The Bad, and Why: Unveiling Emotions in Generative AI*
Cheng Li1,2, Jindong Wang1â , Yixuan Zhang3, Kaijie Zhu1, Xinyi Wang4, Wenxin Hou1, Jianxun Lian1, Fang Luo4, Qiang Yang5, Xing Xie1 1Microsoft Research 2Institute of Software, CAS 3Wi... | {
"id": "2210.09261"
} |
2312.00752 | Mamba: Linear-Time Sequence Modeling with Selective State Spaces | Foundation models, now powering most of the exciting applications in deep
learning, are almost universally based on the Transformer architecture and its
core attention module. Many subquadratic-time architectures such as linear
attention, gated convolution and recurrent models, and structured state space
models (SSMs) ... | http://arxiv.org/pdf/2312.00752 | Albert Gu, Tri Dao | cs.LG, cs.AI | null | null | cs.LG | 20231201 | 20231201 | # Mamba: Linear-Time Sequence Modeling with Selective State Spaces
# Albert Gu*1 and Tri Dao*2
1Machine Learning Department, Carnegie Mellon University 2Department of Computer Science, Princeton University agu@cs.cmu.edu, tri@tridao.me
# Abstract
Foundation models, now powering most of the exciting applications in deep... | {
"id": "2302.13971"
} |
2311.15296 | UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation | Large language models (LLMs) have emerged as pivotal contributors in
contemporary natural language processing and are increasingly being applied
across a diverse range of industries. However, these large-scale probabilistic
statistical models cannot currently ensure the requisite quality in
professional content generat... | http://arxiv.org/pdf/2311.15296 | Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Zhaohui Wy, Dawei He, Peng Cheng, Zhonghao Wang, Haiying Deng | cs.CL | 13 Pages, submitted to ICDE2024 | null | cs.CL | 20231126 | 20231126 | 3 2 0 2
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# UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation
Xun Liang*, Shichao Song*, Simin Niu*, Zhiyu Lit, Feiyu Xiong", Bo Tang", Zhaohui wy', Dawei He!, Peng Cheng', Zhonghao Wang", Haiying Deng? *School... | {
"id": "2307.03109"
} |
2311.04254 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing t... | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | 3 2 0 2
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EVERYTHING OF THOUGHTS : DEFYING THE LAW OF PENROSE TRIANGLE FOR THOUGHT GENERATION
Ruomeng Ding1,2, Chaoyun Zhang1, Lu Wang1, Yong Xu1, Minghua Ma1, Wei Zhang3, Si Qin1, Saravan Rajmohan1, Qingwei Lin1 & Dongmei Zhang1 1Microsoft 2Georgia Institute o... | {
"id": "1706.06708"
} |
2311.04072 | Beyond Imitation: Leveraging Fine-grained Quality Signals for Alignment | Alignment with human preference is a desired property of large language
models (LLMs). Currently, the main alignment approach is based on reinforcement
learning from human feedback (RLHF). Despite the effectiveness of RLHF, it is
intricate to implement and train, thus recent studies explore how to develop
alternative a... | http://arxiv.org/pdf/2311.04072 | Geyang Guo, Ranchi Zhao, Tianyi Tang, Wayne Xin Zhao, Ji-Rong Wen | cs.CL | null | null | cs.CL | 20231107 | 20231107 | 3 2 0 2
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Preprint.
# BEYOND IMITATION: LEVERAGING FINE-GRAINED QUALITY SIGNALS FOR ALIGNMENT
Geyang Guo1â, Ranchi Zhao1â, Tianyi Tang1, Wayne Xin Zhao1,3â , Ji-Rong Wen1,2,3 1Gaoling School of Artificial Intelligence, Renmin University of China. 2School ... | {
"id": "2309.00267"
} |
2311.01964 | Don't Make Your LLM an Evaluation Benchmark Cheater | Large language models~(LLMs) have greatly advanced the frontiers of
artificial intelligence, attaining remarkable improvement in model capacity. To
assess the model performance, a typical approach is to construct evaluation
benchmarks for measuring the ability level of LLMs in different aspects.
Despite that a number o... | http://arxiv.org/pdf/2311.01964 | Kun Zhou, Yutao Zhu, Zhipeng Chen, Wentong Chen, Wayne Xin Zhao, Xu Chen, Yankai Lin, Ji-Rong Wen, Jiawei Han | cs.CL, cs.AI | 11 pages | null | cs.CL | 20231103 | 20231103 | 3 2 0 2
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# Donât Make Your LLM an Evaluation Benchmark Cheater
Kun Zhou1, Yutao Zhu2, Zhipeng Chen2, Wentong Chen2, Wayne Xin Zhao2 Xu Chen2, Yankai Lin2, Ji-Rong Wen1,2 and Jiawei Han3 1 School of Information, Renmin University of China 2 Gaoling School of Art... | {
"id": "2310.18018"
} |
2311.04915 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy... | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | # Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models
Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, & Sowon Hahn Human Factors Psychology Lab Seoul National University yoonlee78@snu.ac.kr, swhahn@snu.ac.kr
Standard Prompting Input: | just broke up. My life is ov... | {
"id": "2302.13971"
} |
2311.01555 | Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers | Recent studies have demonstrated the great potential of Large Language Models
(LLMs) serving as zero-shot relevance rankers. The typical approach involves
making comparisons between pairs or lists of documents. Although effective,
these listwise and pairwise methods are not efficient and also heavily rely on
intricate ... | http://arxiv.org/pdf/2311.01555 | Weiwei Sun, Zheng Chen, Xinyu Ma, Lingyong Yan, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, Zhaochun Ren | cs.IR, cs.CL | null | null | cs.IR | 20231102 | 20231102 | 3 2 0 2 v o N 2 ] R I . s c [
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# Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers
Weiwei Sun1 Zheng Chen1 Xinyu Ma2 Pengjie Ren1 Zhumin Chen1 Dawei Yin2 Zhaochun Ren3 1Shandong University, Qingdao, China 3Leiden University, Leiden, The Netherlands {sunn... | {
"id": "2210.11416"
} |
2311.01343 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading ... | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | 3 2 0 2 v o N 8 ] R I . s c [
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Collaborative Large Language Model for Recommender Systems Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1 1University of Virginia, 2LinkedIn Inc. 1{uqp4qh, jundong}@virginia.edu, 2{liawu, qguo, liahong}@linkedin.com
Liangjie Hongâ, J... | {
"id": "2302.13971"
} |
2310.19341 | Skywork: A More Open Bilingual Foundation Model | In this technical report, we present Skywork-13B, a family of large language
models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both
English and Chinese texts. This bilingual foundation model is the most
extensively trained and openly published LLMs of comparable size to date. We
introduce a two-s... | http://arxiv.org/pdf/2310.19341 | Tianwen Wei, Liang Zhao, Lichang Zhang, Bo Zhu, Lijie Wang, Haihua Yang, Biye Li, Cheng Cheng, Weiwei Lü, Rui Hu, Chenxia Li, Liu Yang, Xilin Luo, Xuejie Wu, Lunan Liu, Wenjun Cheng, Peng Cheng, Jianhao Zhang, Xiaoyu Zhang, Lei Lin, Xiaokun Wang, Yutuan Ma, Chuanhai Dong, Yanqi Sun, Yifu Chen, Yongyi Peng, Xiaojuan Lia... | cs.CL, cs.AI | null | null | cs.CL | 20231030 | 20231030 | 3 2 0 2
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# Skywork: A More Open Bilingual Foundation Model
Tianwen Wei, Liang Zhao, Lichang Zhang, Bo Zhu, Lijie Wang, Haihua Yang, Biye Li, Cheng Cheng, Weiwei Lü, Rui Hu Chenxia Li, Liu Yang, Xilin Luo, Xuejie Wu, Lunan Liu, Wenjun Cheng, Peng Cheng, Jianhao... | {
"id": "2309.05463"
} |
2310.18018 | NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark | In this position paper, we argue that the classical evaluation on Natural
Language Processing (NLP) tasks using annotated benchmarks is in trouble. The
worst kind of data contamination happens when a Large Language Model (LLM) is
trained on the test split of a benchmark, and then evaluated in the same
benchmark. The ex... | http://arxiv.org/pdf/2310.18018 | Oscar Sainz, Jon Ander Campos, Iker García-Ferrero, Julen Etxaniz, Oier Lopez de Lacalle, Eneko Agirre | cs.CL | Accepted at EMNLP2024-Findings | null | cs.CL | 20231027 | 20231027 | 3 2 0 2
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NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark Oscar Sainz1 Jon Ander Campos2 Iker GarcÃa-Ferrero1 Julen Etxaniz1 Oier Lopez de Lacalle1 Eneko Agirre1 1 HiTZ Center - Ixa, University of the Basque Country U... | {
"id": "2103.03874"
} |
2310.16789 | Detecting Pretraining Data from Large Language Models | Although large language models (LLMs) are widely deployed, the data used to
train them is rarely disclosed. Given the incredible scale of this data, up to
trillions of tokens, it is all but certain that it includes potentially
problematic text such as copyrighted materials, personally identifiable
information, and test... | http://arxiv.org/pdf/2310.16789 | Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, Luke Zettlemoyer | cs.CL, cs.CR, cs.LG | null | null | cs.CL | 20231025 | 20231103 | 3 2 0 2
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# DETECTING PRETRAINING DATA FROM LARGE LAN- GUAGE MODELS
Weijia Shi1 â Anirudh Ajith2â Mengzhou Xia2 Yangsibo Huang2 Daogao Liu1 Terra Blevins1 Danqi Chen2 Luke Zettlemoyer1 1University of Washington swj0419.github.io/detect-pretrain.github.io
# ABS... | {
"id": "2012.13891"
} |
2310.14122 | Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels | Zero-shot text rankers powered by recent LLMs achieve remarkable ranking
performance by simply prompting. Existing prompts for pointwise LLM rankers
mostly ask the model to choose from binary relevance labels like "Yes" and
"No". However, the lack of intermediate relevance label options may cause the
LLM to provide noi... | http://arxiv.org/pdf/2310.14122 | Honglei Zhuang, Zhen Qin, Kai Hui, Junru Wu, Le Yan, Xuanhui Wang, Michael Bendersky | cs.IR | 13 pages | null | cs.IR | 20231021 | 20231106 | 3 2 0 2
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# Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels
Honglei Zhuang, Zhen Qin, Kai Hui, Junru Wu, Le Yan, Xuanhui Wang and Michael Bendersky Google Research {hlz,zhenqin,kaihuibj,junru,lyyanle, xuanhui,bemike}@go... | {
"id": "2305.06474"
} |
2310.12773 | Safe RLHF: Safe Reinforcement Learning from Human Feedback | With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we pro... | http://arxiv.org/pdf/2310.12773 | Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang | cs.AI, cs.LG | null | null | cs.AI | 20231019 | 20231019 | 3 2 0 2
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# SAFE RLHF: SAFE REINFORCEMENT LEARNING FROM HUMAN FEEDBACK
Josef Daiâ Xuehai Panâ Ruiyang Sunâ Jiaming Jiâ Xinbo Xu Mickel Liu Yizhou Wang Yaodong Yang
# Peking University
{jtd.acad,rockmagma02,jiamg.ji,xux98750,mickelliu7}@gmail.com {XuehaiP... | {
"id": "2302.13971"
} |
2310.12397 | GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists... | http://arxiv.org/pdf/2310.12397 | Kaya Stechly, Matthew Marquez, Subbarao Kambhampati | cs.AI | 18 pages, 3 figures | null | cs.AI | 20231019 | 20231019 | 3 2 0 2
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# GPT-4 Doesnât Know Itâs Wrong: An Analysis of Iterative Prompting for Reasoning Problems
Kaya Stechly* Matthew Marquez* Subbarao Kambhampati*
# Abstract
There has been considerable divergence of opinion on the reasoning abilities of Large Languag... | {
"id": "2206.10498"
} |
2310.10631 | Llemma: An Open Language Model For Mathematics | We present Llemma, a large language model for mathematics. We continue
pretraining Code Llama on the Proof-Pile-2, a mixture of scientific papers, web
data containing mathematics, and mathematical code, yielding Llemma. On the
MATH benchmark Llemma outperforms all known open base models, as well as the
unreleased Miner... | http://arxiv.org/pdf/2310.10631 | Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, Marco Dos Santos, Stephen McAleer, Albert Q. Jiang, Jia Deng, Stella Biderman, Sean Welleck | cs.CL, cs.AI, cs.LO | Updated references; corrected description of COPRA search budget | null | cs.CL | 20231016 | 20231201 | 3 2 0 2 c e D 1
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Preprint.
# LLEMMA: AN OPEN LANGUAGE MODEL FOR MATHEMATICS
Zhangir Azerbayev 1,2 Hailey Schoelkopf 2 Keiran Paster 3,4
Marco Dos Santos 5 Stephen McAleer 6 Albert Q. Jiang 5 Jia Deng 1
# Stella Biderman 2
# Sean Welleck 6,7
1 Princeton University 2 Eleut... | {
"id": "2308.09583"
} |
2310.09497 | A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models | Large Language Models (LLMs) demonstrate impressive effectiveness in
zero-shot document ranking tasks. Pointwise, Pairwise, and Listwise prompting
approaches have been proposed for LLM-based zero-shot ranking. Our study begins
by thoroughly evaluating these existing approaches within a consistent
experimental framework... | http://arxiv.org/pdf/2310.09497 | Shengyao Zhuang, Honglei Zhuang, Bevan Koopman, Guido Zuccon | cs.IR, cs.AI | 9 pages | null | cs.IR | 20231014 | 20231014 | 3 2 0 2
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# A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
Honglei Zhuang Google Research hlz@google.com
# Bevan Koopman CSIRO bevan.koopman@csiro.au
Guido Zuccon The University of Queensland g.zuccon@uq.edu.au... | {
"id": "2302.13971"
} |
2310.09611 | VizAbility: Multimodal Accessible Data Visualization with Keyboard Navigation and Conversational Interaction | Data visualization serves as a crucial tool for communicating important
information in our society. Yet, as visualizations grow more complex, they
become less accessible to individuals with visual impairments. Traditional
accessibility approaches like alternative text and data tables often fall short
of capturing the f... | http://arxiv.org/pdf/2310.09611 | Joshua Gorniak, Yoon Kim, Stephen Gwon, Donglai Wei, Nam Wook Kim | cs.HC | 13 pages, 7 figures | null | cs.HC | 20231014 | 20231014 | 3 2 0 2
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# VizAbility: Multimodal Accessible Data Visualization with Keyboard Navigation and Conversational Interaction
Joshua Gorniak joshua.gorniak@bc.edu Boston College Chestnut Hill, Massachusetts, USA
# Yoon Kim yoonkim@mit.edu MIT Cambridge, Massachuset... | {
"id": "2303.04048"
} |
2310.08118 | Can Large Language Models Really Improve by Self-critiquing Their Own Plans? | There have been widespread claims about Large Language Models (LLMs) being
able to successfully verify or self-critique their candidate solutions in
reasoning problems in an iterative mode. Intrigued by those claims, in this
paper we set out to investigate the verification/self-critiquing abilities of
large language mo... | http://arxiv.org/pdf/2310.08118 | Karthik Valmeekam, Matthew Marquez, Subbarao Kambhampati | cs.AI | null | null | cs.AI | 20231012 | 20231012 | 3 2 0 2
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# Can Large Language Models Really Improve by Self-critiquing Their Own Plans?
# Karthik Valmeekamâ School of Computing & AI Arizona State University Tempe. kvalmeek@asu.edu
# Matthew Marquezâ School of Computing & AI Arizona State University, Temp... | {
"id": "2305.10601"
} |
2310.08319 | Fine-Tuning LLaMA for Multi-Stage Text Retrieval | The effectiveness of multi-stage text retrieval has been solidly demonstrated
since before the era of pre-trained language models. However, most existing
studies utilize models that predate recent advances in large language models
(LLMs). This study seeks to explore potential improvements that
state-of-the-art LLMs can... | http://arxiv.org/pdf/2310.08319 | Xueguang Ma, Liang Wang, Nan Yang, Furu Wei, Jimmy Lin | cs.IR | null | null | cs.IR | 20231012 | 20231012 | 3 2 0 2
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# Fine-Tuning LLaMA for Multi-Stage Text Retrieval
# Xueguang Ma â Liang Wang â¡ Nan Yang â¡ Furu Wei â¡ Jimmy Lin â â David R. Cheriton School of Computer Science, University of Waterloo â¡ Microsoft Research
# Abstract
The effectiveness of ... | {
"id": "2302.13971"
} |
2310.07712 | Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models | Large language models (LLMs) exhibit positional bias in how they use context,
which especially complicates listwise ranking. To address this, we propose
permutation self-consistency, a form of self-consistency over ranking list
outputs of black-box LLMs. Our key idea is to marginalize out different list
orders in the p... | http://arxiv.org/pdf/2310.07712 | Raphael Tang, Xinyu Zhang, Xueguang Ma, Jimmy Lin, Ferhan Ture | cs.CL, cs.LG | First two authors contributed equally; 10 pages, 6 figures | null | cs.CL | 20231011 | 20231011 | 3 2 0 2
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# Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models
# Raphael Tang,â1 Xinyu Zhang,â2 Xueguang Ma,2 Jimmy Lin,2 Ferhan Ture1 1Comcast Applied AI 2University of Waterloo
1{raphael_tang, ferhan_ture}@... | {
"id": "2305.17926"
} |
2310.06825 | Mistral 7B | We introduce Mistral 7B v0.1, a 7-billion-parameter language model engineered
for superior performance and efficiency. Mistral 7B outperforms Llama 2 13B
across all evaluated benchmarks, and Llama 1 34B in reasoning, mathematics, and
code generation. Our model leverages grouped-query attention (GQA) for faster
inferenc... | http://arxiv.org/pdf/2310.06825 | Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed | cs.CL, cs.AI, cs.LG | Models and code are available at
https://mistral.ai/news/announcing-mistral-7b/ | null | cs.CL | 20231010 | 20231010 | 3 2 0 2
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# Mistral 7B
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock,... | {
"id": "2302.13971"
} |
2310.05910 | SALMON: Self-Alignment with Principle-Following Reward Models | Supervised Fine-Tuning (SFT) on response demonstrations combined with
Reinforcement Learning from Human Feedback (RLHF) constitutes a powerful
paradigm for aligning LLM-based AI agents. However, a significant limitation of
such an approach is its dependency on high-quality human annotations, making
its application to i... | http://arxiv.org/pdf/2310.05910 | Zhiqing Sun, Yikang Shen, Hongxin Zhang, Qinhong Zhou, Zhenfang Chen, David Cox, Yiming Yang, Chuang Gan | cs.CL, cs.AI, cs.LG | Project page: https://github.com/IBM/SALMON | null | cs.CL | 20231009 | 20231009 | 3 2 0 2
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Preprint
# SALMON: SELF-ALIGNMENT WITH PRINCIPLE-FOLLOWING REWARD MODELS
# Zhiqing Sun1,2â Yikang Shen1 Hongxin Zhang3 Qinhong Zhou3
# Zhenfang Chen1
# David Cox1
# Yiming Yang2
# Chuang Gan1,3
1MIT-IBM Watson AI Lab, IBM Research 2Language Technologie... | {
"id": "2302.13971"
} |
2310.03214 | FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation | Most large language models (LLMs) are trained once and never updated; thus,
they lack the ability to dynamically adapt to our ever-changing world. In this
work, we perform a detailed study of the factuality of LLM-generated text in
the context of answering questions that test current world knowledge.
Specifically, we i... | http://arxiv.org/pdf/2310.03214 | Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, Thang Luong | cs.CL | Preprint, 26 pages, 10 figures, 5 tables; Added FreshEval | null | cs.CL | 20231005 | 20231122 | 3 2 0 2
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Preprint
# FRESHLLMS: REFRESHING LARGE LANGUAGE MODELS WITH SEARCH ENGINE AUGMENTATION
Tu Vu1 Mohit Iyyer2 Xuezhi Wang1 Noah Constant1 Jerry Wei1 Google1 University of Massachusetts Amherst2 freshllms@google.com OpenAI3
# ABSTRACT
Most large language m... | {
"id": "2203.05115"
} |
2310.02304 | Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation | Several recent advances in AI systems (e.g., Tree-of-Thoughts and
Program-Aided Language Models) solve problems by providing a "scaffolding"
program that structures multiple calls to language models to generate better
outputs. A scaffolding program is written in a programming language such as
Python. In this work, we u... | http://arxiv.org/pdf/2310.02304 | Eric Zelikman, Eliana Lorch, Lester Mackey, Adam Tauman Kalai | cs.CL, cs.AI, cs.LG, stat.ML | null | null | cs.CL | 20231003 | 20231003 | 3 2 0 2
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# SELF-TAUGHT OPTIMIZER (STOP): RECURSIVELY SELF-IMPROVING CODE GENERATION
Eric Zelikman1,2, Eliana Lorch, Lester Mackey1, Adam Tauman Kalai1 1Microsoft Research, 2Stanford University
# ABSTRACT
Several recent advances in AI systems (e.g., Tree-of-Though... | {
"id": "2305.17126"
} |
2310.06775 | Conceptual Framework for Autonomous Cognitive Entities | The rapid development and adoption of Generative AI (GAI) technology in the
form of chatbots such as ChatGPT and Claude has greatly increased interest in
agentic machines. This paper introduces the Autonomous Cognitive Entity (ACE)
model, a novel framework for a cognitive architecture, enabling machines and
software ag... | http://arxiv.org/pdf/2310.06775 | David Shapiro, Wangfan Li, Manuel Delaflor, Carlos Toxtli | cs.HC, cs.AI, H.4.0 | 34 pages, 12 figures | null | cs.HC | 20231003 | 20231101 | 3 2 0 2
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# Conceptual Framework for Autonomous Cognitive Entities
DAVID SHAPIRO, Human-AI Empowerment Lab at Clemson University, USA WANGFAN LI, Human-AI Empowerment Lab at Clemson University, USA MANUEL DELAFLOR, Human-AI Empowerment Lab at Clemson University, U... | {
"id": "1712.05474"
} |
2310.04450 | Investigating Large Language Models' Perception of Emotion Using Appraisal Theory | Large Language Models (LLM) like ChatGPT have significantly advanced in
recent years and are now being used by the general public. As more people
interact with these systems, improving our understanding of these black box
models is crucial, especially regarding their understanding of human
psychological aspects. In thi... | http://arxiv.org/pdf/2310.04450 | Nutchanon Yongsatianchot, Parisa Ghanad Torshizi, Stacy Marsella | cs.CL, cs.AI | null | 11th International Conference on Affective Computing and
Intelligent Interaction Workshop and Demo (ACIIW) 2023 1-8 | cs.CL | 20231003 | 20231003 | 3 2 0 2
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2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)
# Investigating Large Language Modelsâ Perception of Emotion Using Appraisal Theory
Nutchanon Yongsatianchot Khoury College of Computer Sc... | {
"id": "2302.02083"
} |
2310.02263 | Contrastive Post-training Large Language Models on Data Curriculum | Alignment serves as an important step to steer large language models (LLMs)
towards human preferences. In this paper, we explore contrastive post-training
techniques for alignment by automatically constructing preference pairs from
multiple models of varying strengths (e.g., InstructGPT, ChatGPT and GPT-4). We
carefull... | http://arxiv.org/pdf/2310.02263 | Canwen Xu, Corby Rosset, Luciano Del Corro, Shweti Mahajan, Julian McAuley, Jennifer Neville, Ahmed Hassan Awadallah, Nikhil Rao | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20231003 | 20231003 | 3 2 0 2
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Preprint
# CONTRASTIVE POST-TRAINING LARGE LANGUAGE MODELS ON DATA CURRICULUM
Canwen Xu1â, Corby Rosset2â, Luciano Del Corro2, Shweti Mahajan2, Julian McAuley1, Jennifer Neville2, Ahmed Hassan Awadallah2, Nikhil Rao2 1University of California, San Di... | {
"id": "2309.00267"
} |
2310.02255 | MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts | Large Language Models (LLMs) and Large Multimodal Models (LMMs) exhibit
impressive problem-solving skills in many tasks and domains, but their ability
in mathematical reasoning in visual contexts has not been systematically
studied. To bridge this gap, we present MathVista, a benchmark designed to
combine challenges fr... | http://arxiv.org/pdf/2310.02255 | Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, Jianfeng Gao | cs.CV, cs.AI, cs.CL, cs.LG | 116 pages, 120 figures. Accepted to ICLR 2024 | null | cs.CV | 20231003 | 20240121 | 4 2 0 2
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Published as a conference paper at ICLR 2024
MATHVISTA: EVALUATING MATHEMATICAL REASON- ING OF FOUNDATION MODELS IN VISUAL CONTEXTS
Pan Lu1,3, Hritik Bansal1, Tony Xia1, Jiacheng Liu2, Chunyuan Li3, Hannaneh Hajishirzi2, Hao Cheng3, Kai-Wei Chang1, Mic... | {
"id": "2302.13971"
} |
2310.02174 | Ask Again, Then Fail: Large Language Models' Vacillations in Judgement | With the emergence of generative conversational large language models (LLMs)
like ChatGPT, serving as virtual assistants in various fields, the stability
and reliability of their responses have become crucial. However, during usage,
it has been observed that these models tend to waver in their judgements when
confronte... | http://arxiv.org/pdf/2310.02174 | Qiming Xie, Zengzhi Wang, Yi Feng, Rui Xia | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20231003 | 20231003 | 3 2 0 2
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Under Review
ASK AGAIN, THEN FAIL: LARGE LANGUAGE MOD- ELSâ VACILLATIONS IN JUDGEMENT
â
Qiming Xieâ Zengzhi Wangâ â School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China {qmxie, zzwang, yfeng, r... | {
"id": "2302.13971"
} |
2310.01386 | Who is ChatGPT? Benchmarking LLMs' Psychological Portrayal Using PsychoBench | Large Language Models (LLMs) have recently showcased their remarkable
capacities, not only in natural language processing tasks but also across
diverse domains such as clinical medicine, legal consultation, and education.
LLMs become more than mere applications, evolving into assistants capable of
addressing diverse us... | http://arxiv.org/pdf/2310.01386 | Jen-tse Huang, Wenxuan Wang, Eric John Li, Man Ho Lam, Shujie Ren, Youliang Yuan, Wenxiang Jiao, Zhaopeng Tu, Michael R. Lyu | cs.CL | Accepted for ICLR 2024 Oral Presentation. 15 pages (main text) and 5
pages (appendix) | null | cs.CL | 20231002 | 20240122 | 4 2 0 2 n a J 2 2 ] L C . s c [
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Published as a conference paper at ICLR 2024
# WHO IS CHATGPT? BENCHMARKING LLMSâ PSYCHOLOGICAL PORTRAYAL USING PSYCHOBENCH
Jen-tse Huang1,3, Wenxuan Wang1,3, Eric John Li1, Man Ho Lam1, Shujie Ren2, Youliang Yuan3,4, Wenxiang Jiao3â, Zhaopeng Tu3,... | {
"id": "2303.13648"
} |
2310.00754 | Analyzing and Mitigating Object Hallucination in Large Vision-Language Models | Large vision-language models (LVLMs) have shown remarkable abilities in
understanding visual information with human languages. However, LVLMs still
suffer from object hallucination, which is the problem of generating
descriptions that include objects that do not actually exist in the images.
This can negatively impact ... | http://arxiv.org/pdf/2310.00754 | Yiyang Zhou, Chenhang Cui, Jaehong Yoon, Linjun Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, Huaxiu Yao | cs.LG, cs.CL, cs.CV | null | null | cs.LG | 20231001 | 20231001 | 3 2 0 2
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Preprint
# ANALYZING AND MITIGATING OBJECT HALLUCINA- TION IN LARGE VISION-LANGUAGE MODELS
Yiyang Zhou1â Chenhang Cui1â Chelsea Finn4 Mohit Bansal1 Huaxiu Yao1 1UNC-Chapel Hill, 2Rutgers University, 3Columbia University, 4Stanford University zhouyiya... | {
"id": "2308.14972"
} |
2309.16609 | Qwen Technical Report | Large language models (LLMs) have revolutionized the field of artificial
intelligence, enabling natural language processing tasks that were previously
thought to be exclusive to humans. In this work, we introduce Qwen, the first
installment of our large language model series. Qwen is a comprehensive
language model seri... | http://arxiv.org/pdf/2309.16609 | Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wan... | cs.CL | 59 pages, 5 figures | null | cs.CL | 20230928 | 20230928 | 3 2 0 2
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# QWEN TECHNICAL REPORT
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui M... | {
"id": "2305.20050"
} |
2309.16797 | Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution | Popular prompt strategies like Chain-of-Thought Prompting can dramatically
improve the reasoning abilities of Large Language Models (LLMs) in various
domains. However, such hand-crafted prompt-strategies are often sub-optimal. In
this paper, we present Promptbreeder, a general-purpose self-referential
self-improvement ... | http://arxiv.org/pdf/2309.16797 | Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel | cs.CL, cs.AI, cs.LG, cs.NE | null | null | cs.CL | 20230928 | 20230928 | 3 2 0 2
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© Google DeepMind
# PROMPTBREEDER: SELF-REFERENTIAL SELF-IMPROVEMENT VIA PROMPT EVOLUTION
Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rockt¨aschel
# Google DeepMind {chrisantha,dylski,henrykm,osindero,rocktaschel}@goog... | {
"id": "2305.03495"
} |
2309.15088 | RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models | Researchers have successfully applied large language models (LLMs) such as
ChatGPT to reranking in an information retrieval context, but to date, such
work has mostly been built on proprietary models hidden behind opaque API
endpoints. This approach yields experimental results that are not reproducible
and non-determin... | http://arxiv.org/pdf/2309.15088 | Ronak Pradeep, Sahel Sharifymoghaddam, Jimmy Lin | cs.IR, cs.CL | null | null | cs.IR | 20230926 | 20230926 | 3 2 0 2
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# RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models
# Ronak Pradeepââ, Sahel Sharifymoghaddamâ, Jimmy Lin
# David R. Cheriton School of Computer Science, University of Waterloo, Canada
{rpradeep, sahel.shari... | {
"id": "2301.02998"
} |
2309.14525 | Aligning Large Multimodal Models with Factually Augmented RLHF | Large Multimodal Models (LMM) are built across modalities and the
misalignment between two modalities can result in "hallucination", generating
textual outputs that are not grounded by the multimodal information in context.
To address the multimodal misalignment issue, we adapt the Reinforcement
Learning from Human Fee... | http://arxiv.org/pdf/2309.14525 | Zhiqing Sun, Sheng Shen, Shengcao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liang-Yan Gui, Yu-Xiong Wang, Yiming Yang, Kurt Keutzer, Trevor Darrell | cs.CV, cs.CL | Preprint | null | cs.CV | 20230925 | 20230925 | 3 2 0 2
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Preprint
ALIGNING LARGE MULTIMODAL MODELS WITH FACTUALLY AUGMENTED RLHF
Zhiqing Sunââ , Sheng Shenââ£, Shengcao Caoâ ⢠Haotian Liuâ¡, Chunyuan Liâ®, Yikang Shenâ³, Chuang Ganâ ââ³, Liang-Yan Guiâ ⢠Yu-Xiong Wangâ â¢, Yiming Yangâ... | {
"id": "2302.13971"
} |
2309.14365 | An In-depth Survey of Large Language Model-based Artificial Intelligence Agents | Due to the powerful capabilities demonstrated by large language model (LLM),
there has been a recent surge in efforts to integrate them with AI agents to
enhance their performance. In this paper, we have explored the core differences
and characteristics between LLM-based AI agents and traditional AI agents.
Specificall... | http://arxiv.org/pdf/2309.14365 | Pengyu Zhao, Zijian Jin, Ning Cheng | cs.CL, cs.AI | null | null | cs.CL | 20230923 | 20230923 | 3 2 0 2
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# An In-depth Survey of Large Language Model-based Artificial Intelligence Agents
Pengyu Zhaoâ, Zijian Jinâ, Ning Cheng Beijing Jiaotong University, New York University, zj2076@nyu.edu {pengyuzhao, ningcheng}@bjtu.edu.cn
Abstract Due to the powerfu... | {
"id": "2306.05424"
} |
2309.12284 | MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models | Large language models (LLMs) have pushed the limits of natural language
understanding and exhibited excellent problem-solving ability. Despite the
great success, most existing open-source LLMs (e.g., LLaMA-2) are still far
away from satisfactory for solving mathematical problem due to the complex
reasoning procedures. ... | http://arxiv.org/pdf/2309.12284 | Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu | cs.CL, cs.AI | Technical Report, Work in Progress. Project Page:
https://meta-math.github.io/ | null | cs.CL | 20230921 | 20231009 | 3 2 0 2
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Technical Report
METAMATH: BOOTSTRAP YOUR OWN MATHEMATICAL QUESTIONS FOR LARGE LANGUAGE MODELS
# Jincheng Yu3,4 Zhengying Liu4 James T. Kwok3 Zhenguo Li4 Adrian Weller1,5 Weiyang Liu1,6,â
Longhui Yu1,* Weisen Jiang2,3,* Han Shi4,â Yu Zhang2 1Universi... | {
"id": "2302.13971"
} |
2309.10818 | SlimPajama-DC: Understanding Data Combinations for LLM Training | This paper aims to understand the impacts of various data combinations (e.g.,
web text, wikipedia, github, books) on the training of large language models
using SlimPajama. SlimPajama is a rigorously deduplicated, multi-source
dataset, which has been refined and further deduplicated to 627B tokens from
the extensive 1.... | http://arxiv.org/pdf/2309.10818 | Zhiqiang Shen, Tianhua Tao, Liqun Ma, Willie Neiswanger, Zhengzhong Liu, Hongyi Wang, Bowen Tan, Joel Hestness, Natalia Vassilieva, Daria Soboleva, Eric Xing | cs.CL, cs.AI | Technical report. Huggingface: https://huggingface.co/MBZUAI-LLM and
https://huggingface.co/datasets/cerebras/SlimPajama-627B | null | cs.CL | 20230919 | 20231009 | 3 2 0 2
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# SlimPajama-DC: Understanding Data Combinations for LLM Training
Zhiqiang Shenâ Tianhua Taoâ ,â¡ Liqun Maâ Willie Neiswanger§ Joel Hestness⯠Zhengzhong Liuâ Hongyi Wangâ® Bowen Tanâ®
# Natalia Vassilieva⯠Daria Soboleva⯠Eric Xingâ â... | {
"id": "2302.13971"
} |
2309.10691 | MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback | To solve complex tasks, large language models (LLMs) often require multiple
rounds of interactions with the user, sometimes assisted by external tools.
However, current evaluation protocols often emphasize benchmark performance
with single-turn exchanges, neglecting the nuanced interactions among the user,
LLMs, and ex... | http://arxiv.org/pdf/2309.10691 | Xingyao Wang, Zihan Wang, Jiateng Liu, Yangyi Chen, Lifan Yuan, Hao Peng, Heng Ji | cs.CL, cs.AI, cs.LG | Code is available on our project website:
https://xingyaoww.github.io/mint-bench | null | cs.CL | 20230919 | 20231012 | 3 2 0 2
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Preprint.
# MINT: EVALUATING LLMS IN MULTI-TURN INTER- ACTION WITH TOOLS AND LANGUAGE FEEDBACK
@
Xingyao Wang1â, Zihan Wang1,2ââ , Jiateng Liu1, Yangyi Chen1, Lifan Yuan1â , Hao Peng1, Heng Ji1 1 University of Illinois Urbana-Champaign, 2 Renmin ... | {
"id": "2308.12950"
} |
2309.10621 | Large language models can accurately predict searcher preferences | Relevance labels, which indicate whether a search result is valuable to a
searcher, are key to evaluating and optimising search systems. The best way to
capture the true preferences of users is to ask them for their careful feedback
on which results would be useful, but this approach does not scale to produce a
large n... | http://arxiv.org/pdf/2309.10621 | Paul Thomas, Seth Spielman, Nick Craswell, Bhaskar Mitra | cs.IR, cs.AI, cs.CL, cs.LG | null | null | cs.IR | 20230919 | 20230919 | 3 2 0 2
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# Large language models can accurately predict searcher preferences
PAUL THOMAS, Microsoft, Australia SETH SPIELMAN, Microsoft, USA NICK CRASWELL, Microsoft, USA BHASKAR MITRA, Microsoft Research, Canada
Relevance labels, which indicate whether a searc... | {
"id": "2305.03495"
} |
2309.10305 | Baichuan 2: Open Large-scale Language Models | Large language models (LLMs) have demonstrated remarkable performance on a
variety of natural language tasks based on just a few examples of natural
language instructions, reducing the need for extensive feature engineering.
However, most powerful LLMs are closed-source or limited in their capability
for languages othe... | http://arxiv.org/pdf/2309.10305 | Aiyuan Yang, Bin Xiao, Bingning Wang, Borong Zhang, Ce Bian, Chao Yin, Chenxu Lv, Da Pan, Dian Wang, Dong Yan, Fan Yang, Fei Deng, Feng Wang, Feng Liu, Guangwei Ai, Guosheng Dong, Haizhou Zhao, Hang Xu, Haoze Sun, Hongda Zhang, Hui Liu, Jiaming Ji, Jian Xie, JunTao Dai, Kun Fang, Lei Su, Liang Song, Lifeng Liu, Liyun R... | cs.CL | Baichuan 2 technical report. Github:
https://github.com/baichuan-inc/Baichuan2 | null | cs.CL | 20230919 | 20230920 | 3 2 0 2
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# Baichuan 2: Open Large-scale Language Models
Aiyuan Yang, Bin Xiao, Bingning Wang, Borong Zhang, Chao Yin, Chenxu Lv, Da Pan Dian Wang, Dong Yan, Fan Yang, Fei Deng, Feng Wang, Feng Liu, Guangwei Ai Guosheng Dong, Haizhou Zhao, Hang Xu, Haoze Sun, Ho... | {
"id": "2302.13971"
} |
2309.09971 | MindAgent: Emergent Gaming Interaction | Large Language Models (LLMs) have the capacity of performing complex
scheduling in a multi-agent system and can coordinate these agents into
completing sophisticated tasks that require extensive collaboration. However,
despite the introduction of numerous gaming frameworks, the community has
insufficient benchmarks tow... | http://arxiv.org/pdf/2309.09971 | Ran Gong, Qiuyuan Huang, Xiaojian Ma, Hoi Vo, Zane Durante, Yusuke Noda, Zilong Zheng, Song-Chun Zhu, Demetri Terzopoulos, Li Fei-Fei, Jianfeng Gao | cs.AI, cs.HC, cs.MA | The first three authors contributed equally. 28 pages | null | cs.AI | 20230918 | 20230919 | 3 2 0 2
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# MINDAGENT: EMERGENT GAMING INTERACTION
Ran Gong1â â, Qiuyuan Huang2â¡â, Xiaojian Ma1â, Hoi Vo3, Zane Durante4â , Yusuke Noda3, Zilong Zheng5, Song-Chun Zhu1567, Demetri Terzopoulos1, Li Fei-Fei4, Jianfeng Gao2 1UCLA; 2Microsoft Research, Red... | {
"id": "2307.04721"
} |
2309.09958 | An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models | Visual instruction tuning has recently shown encouraging progress with
open-source large multimodal models (LMM) such as LLaVA and MiniGPT-4. However,
most existing studies of open-source LMM are performed using models with 13B
parameters or smaller. In this paper we present an empirical study of scaling
LLaVA up to 33... | http://arxiv.org/pdf/2309.09958 | Yadong Lu, Chunyuan Li, Haotian Liu, Jianwei Yang, Jianfeng Gao, Yelong Shen | cs.CV, cs.CL | Released at LLaVA Model Zoo:
https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md | null | cs.CV | 20230918 | 20230918 | 2023:
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# An Empirical Study of Scaling Instruction-Tuned Large Multimodal Models
# Yadong Luâ1, Chunyuan Liâ2, Haotian Liu3, Jianwei Yang2, Jianfeng Gao2, Yelong Shen1
1Microsoft Azure AI 2Microsoft Research 3University of WisconsinâMadison
# Ab... | {
"id": "2307.06281"
} |
2309.09150 | Can Large Language Models Understand Real-World Complex Instructions? | Large language models (LLMs) can understand human instructions, showing their
potential for pragmatic applications beyond traditional NLP tasks. However,
they still struggle with complex instructions, which can be either complex task
descriptions that require multiple tasks and constraints, or complex input that
contai... | http://arxiv.org/pdf/2309.09150 | Qianyu He, Jie Zeng, Wenhao Huang, Lina Chen, Jin Xiao, Qianxi He, Xunzhe Zhou, Lida Chen, Xintao Wang, Yuncheng Huang, Haoning Ye, Zihan Li, Shisong Chen, Yikai Zhang, Zhouhong Gu, Jiaqing Liang, Yanghua Xiao | cs.CL, cs.AI | null | null | cs.CL | 20230917 | 20240108 | 4 2 0 2
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# Can Large Language Models Understand Real-World Complex Instructions?
Qianyu He1, Jie Zeng1, Wenhao Huang1, Lina Chen2, Jin Xiao2, Qianxi He1, Xunzhe Zhou1, Lida Chen1, Xintao Wang1, Yuncheng Huang1, Haoning Ye1, Zihan Li1, Shisong Chen4, Yikai Zhang1,... | {
"id": "2204.02311"
} |
2309.09013 | Bridging Dense and Sparse Maximum Inner Product Search | Maximum inner product search (MIPS) over dense and sparse vectors have
progressed independently in a bifurcated literature for decades; the latter is
better known as top-$k$ retrieval in Information Retrieval. This duality exists
because sparse and dense vectors serve different end goals. That is despite the
fact that ... | http://arxiv.org/pdf/2309.09013 | Sebastian Bruch, Franco Maria Nardini, Amir Ingber, Edo Liberty | cs.IR | null | null | cs.IR | 20230916 | 20230916 | # Bridging Dense and Sparse Maximum Inner Product Search
SEBASTIAN BRUCH, Pinecone, USA FRANCO MARIA NARDINI, ISTI-CNR, Italy AMIR INGBER, Pinecone, Israel EDO LIBERTY, Pinecone, USA
3 2 0 2
# EDO LIBERTY, Pinecone, USA
Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a... | {
"id": "2104.05740"
} |
2309.07915 | MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning | Since the resurgence of deep learning, vision-language models (VLMs) enhanced
by large language models (LLMs) have grown exponentially in popularity.
However, while LLMs can utilize extensive background knowledge and task
information with in-context learning, most VLMs still struggle with
understanding complex multi-mo... | http://arxiv.org/pdf/2309.07915 | Haozhe Zhao, Zefan Cai, Shuzheng Si, Xiaojian Ma, Kaikai An, Liang Chen, Zixuan Liu, Sheng Wang, Wenjuan Han, Baobao Chang | cs.CL, cs.AI, cs.CV | Code, dataset, checkpoints, and demos are available at
https://github.com/PKUnlp-icler/MIC | null | cs.CL | 20230914 | 20231002 | 3 2 0 2
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Preprint
# MMICL: EMPOWERING VISION-LANGUAGE MODEL WITH MULTI-MODAL IN-CONTEXT LEARNING
Haozhe ZhaoË1, Zefan CaiË1, Shuzheng SiË1, Xiaojian Ma2, Kaikai An1, Liang Chen1, Zixuan Liu3, Sheng Wang3, Wenjuan Han:4, Baobao Chang:1 1National Key Laboratory ... | {
"id": "2305.15023"
} |
2309.07864 | The Rise and Potential of Large Language Model Based Agents: A Survey | For a long time, humanity has pursued artificial intelligence (AI) equivalent
to or surpassing the human level, with AI agents considered a promising vehicle
for this pursuit. AI agents are artificial entities that sense their
environment, make decisions, and take actions. Many efforts have been made to
develop intelli... | http://arxiv.org/pdf/2309.07864 | Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, Rui Zheng, Xiaoran Fan, Xiao Wang, Limao Xiong, Yuhao Zhou, Weiran Wang, Changhao Jiang, Yicheng Zou, Xiangyang Liu, Zhangyue Yin, Shihan Dou, Rongxiang Weng, Wensen Cheng, Qi Zhang, Wenjuan Qin, Yongyan... | cs.AI, cs.CL | 86 pages, 12 figures | null | cs.AI | 20230914 | 20230919 | 3 2 0 2
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# The Rise and Potential of Large Language Model Based Agents: A Survey
Zhiheng Xiââ , Wenxiang Chenâ, Xin Guoâ, Wei Heâ, Yiwen Dingâ, Boyang Hongâ, Ming Zhangâ, Junzhe Wangâ, Senjie Jinâ, Enyu Zhouâ,
Rui Zheng, Xiaoran Fan, Xiao W... | {
"id": "2305.08982"
} |
2309.07045 | SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions | With the rapid development of Large Language Models (LLMs), increasing
attention has been paid to their safety concerns. Consequently, evaluating the
safety of LLMs has become an essential task for facilitating the broad
applications of LLMs. Nevertheless, the absence of comprehensive safety
evaluation benchmarks poses... | http://arxiv.org/pdf/2309.07045 | Zhexin Zhang, Leqi Lei, Lindong Wu, Rui Sun, Yongkang Huang, Chong Long, Xiao Liu, Xuanyu Lei, Jie Tang, Minlie Huang | cs.CL | 15 pages | null | cs.CL | 20230913 | 20230913 | 3 2 0 2
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# SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions
Zhexin Zhang1, Leqi Lei1, Lindong Wu2, Rui Sun3, Yongkang Huang2, Chong Long4, Xiao Liu5, Xuanyu Lei5, Jie Tang5, Minlie Huang1 1The CoAI group, DCST, Tsinghua... | {
"id": "2308.14508"
} |
2309.05922 | A Survey of Hallucination in Large Foundation Models | Hallucination in a foundation model (FM) refers to the generation of content
that strays from factual reality or includes fabricated information. This
survey paper provides an extensive overview of recent efforts that aim to
identify, elucidate, and tackle the problem of hallucination, with a particular
focus on ``Larg... | http://arxiv.org/pdf/2309.05922 | Vipula Rawte, Amit Sheth, Amitava Das | cs.AI, cs.CL, cs.IR | null | null | cs.AI | 20230912 | 20230912 | 3 2 0 2
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# A Survey of Hallucination in âLargeâ Foundation Models
Vipula Rawte1â, Amit Sheth1, Amitava Das1 1AI Institute, University of South Carolina, USA {vrawte}@mailbox.sc.edu
# Abstract
and question-answering, achieving remarkable lev- els of accura... | {
"id": "2307.12168"
} |
2309.05898 | Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing | This paper investigates the strategic decision-making capabilities of three
Large Language Models (LLMs): GPT-3.5, GPT-4, and LLaMa-2, within the framework
of game theory. Utilizing four canonical two-player games -- Prisoner's
Dilemma, Stag Hunt, Snowdrift, and Prisoner's Delight -- we explore how these
models navigat... | http://arxiv.org/pdf/2309.05898 | Nunzio Lorè, Babak Heydari | cs.GT, cs.AI, cs.CY, cs.HC, econ.TH, 91C99 (Primary), 91A05, 91A10, 91F99 (Secondary), I.2.8; J.4; K.4.m | 25 pages, 12 figures | null | cs.GT | 20230912 | 20230912 | 3 2 0 2
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# Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing
Nunzio Lorè Network Science Institute Multi-Agent Intelligent Complex Systems (MAGICS) Lab Northeastern University, Boston, Massachusetts, USA lora.n@northeastern.edu... | {
"id": "2305.16867"
} |
2309.05463 | Textbooks Are All You Need II: phi-1.5 technical report | We continue the investigation into the power of smaller Transformer-based
language models as initiated by \textbf{TinyStories} -- a 10 million parameter
model that can produce coherent English -- and the follow-up work on
\textbf{phi-1}, a 1.3 billion parameter model with Python coding performance
close to the state-of... | http://arxiv.org/pdf/2309.05463 | Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, Yin Tat Lee | cs.CL, cs.AI | null | null | cs.CL | 20230911 | 20230911 | 3 2 0 2
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# Textbooks Are All You Need II: phi-1.5 technical report
S´ebastien Bubeck Ronen Eldan Suriya Gunasekar Yin Tat Lee
Microsoft Research
# Abstract
We continue the investigation into the power of smaller Transformer-based language models as initiated b... | {
"id": "2302.13971"
} |
2309.04658 | Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf | Communication games, which we refer to as incomplete information games that
heavily depend on natural language communication, hold significant research
value in fields such as economics, social science, and artificial intelligence.
In this work, we explore the problem of how to engage large language models
(LLMs) in co... | http://arxiv.org/pdf/2309.04658 | Yuzhuang Xu, Shuo Wang, Peng Li, Fuwen Luo, Xiaolong Wang, Weidong Liu, Yang Liu | cs.CL | 23 pages, 5 figures and 4 tables | null | cs.CL | 20230909 | 20230909 | 3 2 0 2
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Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf Yuzhuang Xu1, Shuo Wang1, Peng Li2,â, Fuwen Luo1 Xiaolong Wang1, Weidong Liu1,3, Yang Liu1,2,â 1Department of Computer Science & Technology, Tsinghua University, ... | {
"id": "2302.02083"
} |
2309.03852 | FLM-101B: An Open LLM and How to Train It with $100K Budget | Large language models (LLMs) have achieved remarkable success in NLP and
multimodal tasks, among others. Despite these successes, two main challenges
remain in developing LLMs: (i) high computational cost, and (ii) fair and
objective evaluations. In this paper, we report a solution to significantly
reduce LLM training ... | http://arxiv.org/pdf/2309.03852 | Xiang Li, Yiqun Yao, Xin Jiang, Xuezhi Fang, Xuying Meng, Siqi Fan, Peng Han, Jing Li, Li Du, Bowen Qin, Zheng Zhang, Aixin Sun, Yequan Wang | cs.CL, cs.AI | null | null | cs.CL | 20230907 | 20230917 | 3 2 0 2
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# FLM-101B: An Open LLM and How to Train It with $100K Budget
Xiang Li1â , Yiqun Yao1â , Xin Jiang1â , Xuezhi Fang1â , Xuying Meng2, Siqi Fan3, Peng Han3, Jing Li4, Li Du1, Bowen Qin1, Zheng Zhang1, Aixin Sun5, Yequan Wang1â 1Beijing Academy of A... | {
"id": "2306.15595"
} |
2309.03409 | Large Language Models as Optimizers | Optimization is ubiquitous. While derivative-based algorithms have been
powerful tools for various problems, the absence of gradient imposes challenges
on many real-world applications. In this work, we propose Optimization by
PROmpting (OPRO), a simple and effective approach to leverage large language
models (LLMs) as ... | http://arxiv.org/pdf/2309.03409 | Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen | cs.LG, cs.AI, cs.CL | 42 pages, 26 figures, 15 tables. Code at
https://github.com/google-deepmind/opro | null | cs.LG | 20230907 | 20231207 | 3 2 0 2
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© Google DeepMind
# LARGE LANGUAGE MODELS AS OPTIMIZERS
Chengrun Yang* Xuezhi Wang Yifeng Lu Hanxiao Liu Quoc V. Le Denny Zhou Xinyun Chen* Google DeepMind
Equal contribution
# ABSTRACT
Optimization is ubiquitous. While derivative-based algorithms have ... | {
"id": "2205.12548"
} |
2309.02033 | Data-Juicer: A One-Stop Data Processing System for Large Language Models | The immense evolution in Large Language Models (LLMs) has underscored the
importance of massive, heterogeneous, and high-quality data. A data recipe is a
mixture of data from different sources for training LLMs, which plays a vital
role in LLMs' performance. Existing open-source tools for LLM data processing
are mostly... | http://arxiv.org/pdf/2309.02033 | Daoyuan Chen, Yilun Huang, Zhijian Ma, Hesen Chen, Xuchen Pan, Ce Ge, Dawei Gao, Yuexiang Xie, Zhaoyang Liu, Jinyang Gao, Yaliang Li, Bolin Ding, Jingren Zhou | cs.LG, cs.DB, cs.DC | 20 Pages, 10 figures, 9 tables. The system, data recipes, and demos
are continuously maintained at https://github.com/alibaba/data-juicer | null | cs.LG | 20230905 | 20231220 | 3 2 0 2
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# Data-Juicer: A One-Stop Data Processing System for Large Language Models
Daoyuan Chenâ, Yilun Huangâ, Zhijian Maâ, Hesen Chenâ, Xuchen Panâ , Ce Geâ , Dawei Gaoâ , Yuexiang Xie, Zhaoyang Liu, Jinyang Gao, Yaliang Liâ¡, Bolin Dingâ¡, Jing... | {
"id": "2306.11644"
} |
2309.02427 | Cognitive Architectures for Language Agents | Recent efforts have augmented large language models (LLMs) with external
resources (e.g., the Internet) or internal control flows (e.g., prompt
chaining) for tasks requiring grounding or reasoning, leading to a new class of
language agents. While these agents have achieved substantial empirical
success, we lack a syste... | http://arxiv.org/pdf/2309.02427 | Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L. Griffiths | cs.AI, cs.CL, cs.LG, cs.SC | v2 enriched actionable insights and discussions, and polished
abstract and introduction. 18 pages of main content, 12 pages of references,
5 figures. The first two authors contributed equally, order decided by coin
flip. A CoALA-based repo of recent work on language agents:
https://github.com/ysymyth/awesome-la... | null | cs.AI | 20230905 | 20230927 | 3 2 0 2
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# Cognitive Architectures for Language Agents
Theodore R. Sumersâ Shunyu Yaoâ Karthik Narasimhan Thomas L. Griffiths Princeton University {sumers, shunyuy, karthikn, tomg}@princeton.edu
# Abstract
Recent efforts have augmented large language models... | {
"id": "2305.14909"
} |
2309.01660 | Unveiling Theory of Mind in Large Language Models: A Parallel to Single Neurons in the Human Brain | With their recent development, large language models (LLMs) have been found
to exhibit a certain level of Theory of Mind (ToM), a complex cognitive
capacity that is related to our conscious mind and that allows us to infer
another's beliefs and perspective. While human ToM capabilities are believed to
derive from the n... | http://arxiv.org/pdf/2309.01660 | Mohsen Jamali, Ziv M. Williams, Jing Cai | cs.CL, cs.AI | null | null | cs.CL | 20230904 | 20230904 | # Unveiling theory of mind in large language models: A parallel to single neurons in the human brain
Mohsen Jamali1, Ziv M. Williams1,2,3*, Jing Cai1*â
1 Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA. 2 Harvard-MIT Division of Health Sciences and Technology, Boston, MA... | {
"id": "2302.02083"
} |
2309.01219 | Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models | While large language models (LLMs) have demonstrated remarkable capabilities
across a range of downstream tasks, a significant concern revolves around their
propensity to exhibit hallucinations: LLMs occasionally generate content that
diverges from the user input, contradicts previously generated context, or
misaligns ... | http://arxiv.org/pdf/2309.01219 | Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, Shuming Shi | cs.CL, cs.AI, cs.CY, cs.LG | work in progress; 32 pages | null | cs.CL | 20230903 | 20230924 | 3 2 0 2
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# Sirenâs Song in the AI Ocean:
A Survey on Hallucination in Large Language Models Yue Zhangâ â, Yafu Li⢠, Leyang Cuiâ¡â , Deng Caiâ¡ , Lemao Liuâ¡ Tingchen Fuâ , Xinting Huangâ¡ , Enbo Zhaoâ¡ , Yu Zhangâ , Yulong Chen⢠Longyue Wan... | {
"id": "2307.03109"
} |
2309.00986 | ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models | Large language models (LLMs) have recently demonstrated remarkable
capabilities to comprehend human intentions, engage in reasoning, and design
planning-like behavior. To further unleash the power of LLMs to accomplish
complex tasks, there is a growing trend to build agent framework that equips
LLMs, such as ChatGPT, w... | http://arxiv.org/pdf/2309.00986 | Chenliang Li, Hehong Chen, Ming Yan, Weizhou Shen, Haiyang Xu, Zhikai Wu, Zhicheng Zhang, Wenmeng Zhou, Yingda Chen, Chen Cheng, Hongzhu Shi, Ji Zhang, Fei Huang, Jingren Zhou | cs.CL | null | null | cs.CL | 20230902 | 20230902 | 3 2 0 2
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# ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models
Chenliang Li, Hehong Chen, Ming Yanâ, Weizhou Shen, Haiyang Xu, Zhikai Wu Zhicheng Zhang, Wenmeng Zhou, Yingda Chen, Chen Cheng, Hongzhu Shi Ji Zhang, F... | {
"id": "2304.07849"
} |
2309.00667 | Taken out of context: On measuring situational awareness in LLMs | We aim to better understand the emergence of `situational awareness' in large
language models (LLMs). A model is situationally aware if it's aware that it's
a model and can recognize whether it's currently in testing or deployment.
Today's LLMs are tested for safety and alignment before they are deployed. An
LLM could ... | http://arxiv.org/pdf/2309.00667 | Lukas Berglund, Asa Cooper Stickland, Mikita Balesni, Max Kaufmann, Meg Tong, Tomasz Korbak, Daniel Kokotajlo, Owain Evans | cs.CL, cs.LG | null | null | cs.CL | 20230901 | 20230901 | 3 2 0 2
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# Taken out of context: On measuring situational awareness in LLMs
Lukas Berglundâ1 Asa Cooper Sticklandâ2 Mikita Balesniâ3 Max Kaufmannâ4 Meg Tongâ5 Tomasz Korbak6 Daniel Kokotajlo7 Owain Evans8
# Abstract
We aim to better understand the emerg... | {
"id": "2306.12001"
} |
2309.00267 | RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback | Reinforcement learning from human feedback (RLHF) has proven effective in
aligning large language models (LLMs) with human preferences. However,
gathering high-quality human preference labels can be a time-consuming and
expensive endeavor. RL from AI Feedback (RLAIF), introduced by Bai et al.,
offers a promising altern... | http://arxiv.org/pdf/2309.00267 | Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, Sushant Prakash | cs.CL, cs.AI, cs.LG | Added two more tasks and many more experiments and analyses (e.g.
same-size RLAIF, direct RLAIF, cost analysis) | null | cs.CL | 20230901 | 20231201 | 3 2 0 2
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# RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, Sushant Prakash Google Researc... | {
"id": "1707.06347"
} |
2308.16505 | Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations | Recommender models excel at providing domain-specific item recommendations by
leveraging extensive user behavior data. Despite their ability to act as
lightweight domain experts, they struggle to perform versatile tasks such as
providing explanations and engaging in conversations. On the other hand, large
language mode... | http://arxiv.org/pdf/2308.16505 | Xu Huang, Jianxun Lian, Yuxuan Lei, Jing Yao, Defu Lian, Xing Xie | cs.IR, cs.AI | 18 pages, 17 figures, 7 tables | null | cs.IR | 20230831 | 20240130 | 4 2 0 2 n a J 0 3 ] R I . s c [
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# Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
Xu Huang1, Jianxun Lian2*, Yuxuan Lei1, Jing Yao2, Defu Lian1*, Xing Xie2 1School of Computer Science and Technology, University of Science and Technology of Chin... | {
"id": "2302.13971"
} |
2308.15126 | Evaluation and Analysis of Hallucination in Large Vision-Language Models | Large Vision-Language Models (LVLMs) have recently achieved remarkable
success. However, LVLMs are still plagued by the hallucination problem, which
limits the practicality in many scenarios. Hallucination refers to the
information of LVLMs' responses that does not exist in the visual input, which
poses potential risks... | http://arxiv.org/pdf/2308.15126 | Junyang Wang, Yiyang Zhou, Guohai Xu, Pengcheng Shi, Chenlin Zhao, Haiyang Xu, Qinghao Ye, Ming Yan, Ji Zhang, Jihua Zhu, Jitao Sang, Haoyu Tang | cs.LG, cs.AI, cs.CL, cs.CV | 11 pages, 5 figures | null | cs.LG | 20230829 | 20231010 | {junyangwang,jtsang } @bjtu.edu.cn, {zhouyiyangailab } @gmail.com, { guohai.xgh, ym119608} @alibaba-inc.com Evaluation and Analysis of Hallucination in Large Vision-Language Models Junyang Wang**, Yiyang Zhou**, Guohai Xuâ, Pengcheng Shi*, Chenlin Zhao°, Haiyang Xuâ, Qinghao Yeâ, Ming Yanâ, Ji Zhang®, Jihua Z... | {
"id": "2302.13971"
} |
2308.14963 | Vector Search with OpenAI Embeddings: Lucene Is All You Need | We provide a reproducible, end-to-end demonstration of vector search with
OpenAI embeddings using Lucene on the popular MS MARCO passage ranking test
collection. The main goal of our work is to challenge the prevailing narrative
that a dedicated vector store is necessary to take advantage of recent advances
in deep neu... | http://arxiv.org/pdf/2308.14963 | Jimmy Lin, Ronak Pradeep, Tommaso Teofili, Jasper Xian | cs.IR | null | null | cs.IR | 20230829 | 20230829 | 3 2 0 2
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# Vector Search with OpenAI Embeddings: Lucene Is All You Need
Jimmy Lin,1 Ronak Pradeep,1 Tommaso Teofili,2 Jasper Xian1 1 David R. Cheriton School of Computer Science, University of Waterloo 2 Department of Engineering, Roma Tre University
# Abstract... | {
"id": "2110.01529"
} |
2308.14296 | RecMind: Large Language Model Powered Agent For Recommendation | Recent advancements in instructing Large Language Models (LLMs) to utilize
external tools and execute multi-step plans have significantly enhanced their
ability to solve intricate tasks, ranging from mathematical problems to
creative writing. Yet, there remains a notable gap in studying the capacity of
LLMs in respondi... | http://arxiv.org/pdf/2308.14296 | Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, Yingzhen Yang | cs.IR, cs.AI | null | null | cs.IR | 20230828 | 20230828 | 3 2 0 2
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# RecMind: Large Language Model Powered Agent For Recommendation
Yancheng Wang1, Ziyan Jiang2*, Zheng Chen2*, Fan Yang2*, Yingxue Zhou2*, Eunah Cho2, Xing Fan2, Xiaojiang Huang2, Yanbin Lu2, Yingzhen Yang1 1School of Computing and Augmented Intelligenc... | {
"id": "2302.13971"
} |
2308.13724 | ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning | Motivated by the substantial achievements observed in Large Language Models
(LLMs) in the field of natural language processing, recent research has
commenced investigations into the application of LLMs for complex, long-horizon
sequential task planning challenges in robotics. LLMs are advantageous in
offering the poten... | http://arxiv.org/pdf/2308.13724 | Zhehua Zhou, Jiayang Song, Kunpeng Yao, Zhan Shu, Lei Ma | cs.RO, cs.AI | null | null | cs.RO | 20230826 | 20230826 | 3 2 0 2
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# ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning
# Zhehua Zhou University of Alberta zhehua1@ualberta.ca
Jiayang Song University of Alberta jiayan13@ualberta.ca
# Kunpeng Yao Swiss Federal Institute of Te... | {
"id": "2211.09935"
} |
2308.13149 | SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research | Recently, there has been growing interest in using Large Language Models
(LLMs) for scientific research. Numerous benchmarks have been proposed to
evaluate the ability of LLMs for scientific research. However, current
benchmarks are mostly based on pre-collected objective questions. This design
suffers from data leakag... | http://arxiv.org/pdf/2308.13149 | Liangtai Sun, Yang Han, Zihan Zhao, Da Ma, Zhennan Shen, Baocai Chen, Lu Chen, Kai Yu | cs.CL | 12 pages, 17 figures, 12 tables. Under Review | null | cs.CL | 20230825 | 20230825 | 3 2 0 2
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# SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research
Liangtai Sun, Yang Han, Zihan Zhao, Da Ma, Zhennan Shen, Baocai Chen, Lu Chenâ and Kai Yu*. X-LANCE Lab, Department of Computer Science and Engineering Artific... | {
"id": "2307.03109"
} |
2308.12966 | Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond | In this work, we introduce the Qwen-VL series, a set of large-scale
vision-language models (LVLMs) designed to perceive and understand both texts
and images. Starting from the Qwen-LM as a foundation, we endow it with visual
capacity by the meticulously designed (i) visual receptor, (ii) input-output
interface, (iii) 3... | http://arxiv.org/pdf/2308.12966 | Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, Jingren Zhou | cs.CV, cs.CL | Code, demo and models are available at
https://github.com/QwenLM/Qwen-VL | null | cs.CV | 20230824 | 20231013 | 3 2 0 2
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# Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
# Jinze Baiâ Shuai Baiâ Shusheng Yangâ Shijie Wang Sinan Tan Peng Wang Junyang Lin Chang Zhouâ Jingren Zhou
# Alibaba Group Code & Demo & Mod... | {
"id": "2211.01335"
} |
2308.12682 | SayCanPay: Heuristic Planning with Large Language Models using Learnable Domain Knowledge | Large Language Models (LLMs) have demonstrated impressive planning abilities
due to their vast "world knowledge". Yet, obtaining plans that are both
feasible (grounded in affordances) and cost-effective (in plan length), remains
a challenge, despite recent progress. This contrasts with heuristic planning
methods that e... | http://arxiv.org/pdf/2308.12682 | Rishi Hazra, Pedro Zuidberg Dos Martires, Luc De Raedt | cs.AI | Accepted in AAAI 2024. Website:
https://rishihazra.github.io/SayCanPay/ | null | cs.AI | 20230824 | 20240101 | 4 2 0 2
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# SayCanPay: Heuristic Planning with Large Language Models using Learnable Domain Knowledge
# Rishi Hazra1
# Pedro Zuidberg Dos Martires1 Luc De Raedt1,2 2KU Leuven
# {rishi.hazra, pedro.zuidberg-dos-martires, luc.de-raedt}@oru.se https://rishihazra.gith... | {
"id": "2302.13971"
} |
2308.12519 | Rational Decision-Making Agent with Internalized Utility Judgment | Large language models (LLMs) have demonstrated remarkable advancements and
have attracted significant efforts to develop LLMs into agents capable of
executing intricate multi-step decision-making tasks beyond traditional NLP
applications. Existing approaches to LLM-based decision-making predominantly
build upon the man... | http://arxiv.org/pdf/2308.12519 | Yining Ye, Xin Cong, Shizuo Tian, Yujia Qin, Chong Liu, Yankai Lin, Zhiyuan Liu, Maosong Sun | cs.CL | Received 8,6,6,6 scores on ICLR 2024 | null | cs.CL | 20230824 | 20240117 | 4 2 0 2 n a J 7 1 ] L C . s c [
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Preprint
# RATIONAL DECISION-MAKING AGENT WITH INTER- NALIZED UTILITY JUDGMENT
Yining Ye1â, Xin Cong1ââ , Shizuo Tian1, Yujia Qin1, Chong Liu1, Yankai Lin2, Zhiyuan Liu1â , Maosong Sun1 1Tsinghua University 2Renmin University of China
yeyn2001@g... | {
"id": "2305.14318"
} |
2308.12503 | CGMI: Configurable General Multi-Agent Interaction Framework | Benefiting from the powerful capabilities of large language models (LLMs),
agents based on LLMs have shown the potential to address domain-specific tasks
and emulate human behaviors. However, the content generated by these agents
remains somewhat superficial, owing to their limited domain expertise and the
absence of a... | http://arxiv.org/pdf/2308.12503 | Shi Jinxin, Zhao Jiabao, Wang Yilei, Wu Xingjiao, Li Jiawen, He Liang | cs.AI, cs.HC, cs.MA | 11 pages, 15 figures | null | cs.AI | 20230824 | 20230828 | 3 2 0 2
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# CGMI: Configurable General Multi-Agent Interaction Framework
Jinxin Shi1, Jiabao Zhao1*, Yilei Wang1, Xingjiao Wu2, Jiawen Li1, Liang He1 1School of Computer Science and Technology, East China Normal University, Shanghai, China 2School of Computer Sc... | {
"id": "2302.01560"
} |
2308.12284 | D4: Improving LLM Pretraining via Document De-Duplication and Diversification | Over recent years, an increasing amount of compute and data has been poured
into training large language models (LLMs), usually by doing one-pass learning
on as many tokens as possible randomly selected from large-scale web corpora.
While training on ever-larger portions of the internet leads to consistent
performance ... | http://arxiv.org/pdf/2308.12284 | Kushal Tirumala, Daniel Simig, Armen Aghajanyan, Ari S. Morcos | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230823 | 20230823 | 3 2 0 2
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# D4: Improving LLM Pretraining via Document De-Duplication and Diversification
Kushal Tirumala* Meta AI Research Daniel Simig* Meta AI Research Armen Aghajanyan Meta AI Research Ari S. Morcos Meta AI Research
# Abstract
Over recent years, an increasin... | {
"id": "2006.05929"
} |
2308.12033 | PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine | As an effective tool for eliciting the power of Large Language Models (LLMs),
prompting has recently demonstrated unprecedented abilities across a variety of
complex tasks. To further improve the performance, prompt ensemble has
attracted substantial interest for tackling the hallucination and instability
of LLMs. Howe... | http://arxiv.org/pdf/2308.12033 | Chenrui Zhang, Lin Liu, Jinpeng Wang, Chuyuan Wang, Xiao Sun, Hongyu Wang, Mingchen Cai | cs.CL, cs.AI | 8 pages, 4 figures | null | cs.CL | 20230823 | 20230823 | 3 2 0 2
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# PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine
Chenrui Zhang, Lin Liu**, Jinpeng Wang!, Chuyuan Wang', Xiao Sun', Hongyu Wang!', Mingchen Cai! 'Meituan Inc., Beijing, China *Beijing Jiaotong University, Beijing, China â¢chenrui.zhang @... | {
"id": "2305.03495"
} |
2308.11432 | A Survey on Large Language Model based Autonomous Agents | Autonomous agents have long been a prominent research focus in both academic
and industry communities. Previous research in this field often focuses on
training agents with limited knowledge within isolated environments, which
diverges significantly from human learning processes, and thus makes the agents
hard to achie... | http://arxiv.org/pdf/2308.11432 | Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, Ji-Rong Wen | cs.AI, cs.CL | 35 pages, 5 figures, 3 tables | null | cs.AI | 20230822 | 20230907 | 3 2 0 2
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# A Survey on Large Language Model based Autonomous Agents
Lei Wang, Chen Maâ, Xueyang Fengâ, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, Ji-Rong Wen Gaoling School of Artificial In... | {
"id": "2307.03109"
} |
2308.10837 | Leveraging Large Language Models for Pre-trained Recommender Systems | Recent advancements in recommendation systems have shifted towards more
comprehensive and personalized recommendations by utilizing large language
models (LLM). However, effectively integrating LLM's commonsense knowledge and
reasoning abilities into recommendation systems remains a challenging problem.
In this paper, ... | http://arxiv.org/pdf/2308.10837 | Zhixuan Chu, Hongyan Hao, Xin Ouyang, Simeng Wang, Yan Wang, Yue Shen, Jinjie Gu, Qing Cui, Longfei Li, Siqiao Xue, James Y Zhang, Sheng Li | cs.IR | 13 pages, 4 figures | null | cs.IR | 20230821 | 20230821 | 3 2 0 2
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# Leveraging Large Language Models for Pre-trained Recommender Systems
Zhixuan Chu*1, Hongyan Hao*1, Xin Ouyang1, Simeng Wang1, Yan Wang1, Yue Shen1, Jinjie Gu1, Qing Cui1, Longfei Li1, Siqiao Xue1, James Y Zhang1, Sheng Li2 1Ant Group 2University of V... | {
"id": "1810.04805"
} |
2308.10848 | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors | Autonomous agents empowered by Large Language Models (LLMs) have undergone
significant improvements, enabling them to generalize across a broad spectrum
of tasks. However, in real-world scenarios, cooperation among individuals is
often required to enhance the efficiency and effectiveness of task
accomplishment. Hence, ... | http://arxiv.org/pdf/2308.10848 | Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou | cs.CL | Under review. Code at https://github.com/OpenBMB/AgentVerse/ | null | cs.CL | 20230821 | 20231023 | 3 2 0 2
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Preprint
# AGENTVERSE: FACILITATING MULTI-AGENT COLLAB- ORATION AND EXPLORING EMERGENT BEHAVIORS
Weize Chen!*, Yusheng Su!*, Jingwei Zuo', Cheng Yang*â¢, Chenfei Yuanâ, Chi-Min Chan', Heyang Yu', Yaxi Luâ, Yi-Hsin Hungâ, Chen Qianâ, Yujia Qin!... | {
"id": "2308.01862"
} |
2308.10379 | Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models | Current literature, aiming to surpass the "Chain-of-Thought" approach, often
resorts to an external modus operandi involving halting, modifying, and then
resuming the generation process to boost Large Language Models' (LLMs)
reasoning capacities. This mode escalates the number of query requests, leading
to increased co... | http://arxiv.org/pdf/2308.10379 | Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin | cs.CL, cs.AI | null | null | cs.CL | 20230820 | 20230928 | 3 2 0 2
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# Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models
# Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, and Ming Jin
Virginia Tech
# Abstract
Current literature, aiming to surpass the âChain-of-Thoughtâ approac... | {
"id": "2204.02311"
} |
2308.10053 | Large Language Models as Zero-Shot Conversational Recommenders | In this paper, we present empirical studies on conversational recommendation
tasks using representative large language models in a zero-shot setting with
three primary contributions. (1) Data: To gain insights into model behavior in
"in-the-wild" conversational recommendation scenarios, we construct a new
dataset of re... | http://arxiv.org/pdf/2308.10053 | Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian McAuley | cs.IR, cs.AI | Accepted as CIKM 2023 long paper. Longer version is coming soon
(e.g., more details about dataset) | null | cs.IR | 20230819 | 20230819 | 3 2 0 2
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Large Language Models as Zero-Shot Conversational Recommenders Zhouhang Xieâ zhx022@ucsd.edu University of California, San Diego La Jolla, California, USA
# Zhankui Heâ zhh004@eng.ucsd.edu University of California, San Diego La Jolla, California, U... | {
"id": "2302.13971"
} |
2308.09904 | RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents | The rapid evolution of the web has led to an exponential growth in content.
Recommender systems play a crucial role in Human-Computer Interaction (HCI) by
tailoring content based on individual preferences. Despite their importance,
challenges persist in balancing recommendation accuracy with user satisfaction,
addressi... | http://arxiv.org/pdf/2308.09904 | Yubo Shu, Haonan Zhang, Hansu Gu, Peng Zhang, Tun Lu, Dongsheng Li, Ning Gu | cs.IR, cs.AI | null | null | cs.IR | 20230819 | 20231017 | 3 2 0 2
# t c O 7 1
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RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents Yubo Shu Haonan Zhang Hansu Gu School of Computer Science, Fudan School of Computer Science, Fudan Seattle University University United States Shanghai, China Shan... | {
"id": "2305.07961"
} |
2308.09830 | Synergistic Integration of Large Language Models and Cognitive Architectures for Robust AI: An Exploratory Analysis | This paper explores the integration of two AI subdisciplines employed in the
development of artificial agents that exhibit intelligent behavior: Large
Language Models (LLMs) and Cognitive Architectures (CAs). We present three
integration approaches, each grounded in theoretical models and supported by
preliminary empir... | http://arxiv.org/pdf/2308.09830 | Oscar J. Romero, John Zimmerman, Aaron Steinfeld, Anthony Tomasic | cs.AI | AAAI 2023 Fall Symposium | null | cs.AI | 20230818 | 20230928 | 3 2 0 2
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# Synergistic Integration of Large Language Models and Cognitive Architectures for Robust AI: An Exploratory Analysis
# Oscar J. Romero, John Zimmerman, Aaron Steinfeld, Anthony Tomasic
Carnegie Mellon University oscarr@andrew.cmu.edu, johnz@andrew.cmu... | {
"id": "2302.02083"
} |
2308.09687 | Graph of Thoughts: Solving Elaborate Problems with Large Language Models | We introduce Graph of Thoughts (GoT): a framework that advances prompting
capabilities in large language models (LLMs) beyond those offered by paradigms
such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea and primary
advantage of GoT is the ability to model the information generated by an LLM as
an arbitra... | http://arxiv.org/pdf/2308.09687 | Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, Torsten Hoefler | cs.CL, cs.AI, cs.LG | null | Proceedings of the AAAI Conference on Artificial Intelligence 2024
(AAAI'24) | cs.CL | 20230818 | 20231124 | 3 2 0 2
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# Graph of Thoughts: Solving Elaborate Problems with Large Language Models
Maciej Besta1*, Nils Blach1*, Ales Kubicek1, Robert Gerstenberger1, Lukas Gianinazzi1, Joanna Gajda2, Tomasz Lehmann2, MichaÅ Podstawski3, Hubert Niewiadomski2, Piotr Nyczyk2, ... | {
"id": "2302.13971"
} |
2308.09662 | Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment | Larger language models (LLMs) have taken the world by storm with their
massive multi-tasking capabilities simply by optimizing over a next-word
prediction objective. With the emergence of their properties and encoded
knowledge, the risk of LLMs producing harmful outputs increases, making them
unfit for scalable deploym... | http://arxiv.org/pdf/2308.09662 | Rishabh Bhardwaj, Soujanya Poria | cs.CL | null | null | cs.CL | 20230818 | 20230830 | 3 2 0 2
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# Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment
Rishabh Bhardwajâ¡, Soujanya Poriaâ¡ â¡ DeCLaRe Lab, Singapore University of Technology and Design, Singapore rishabh_bhardwaj@mymail.sutd.edu.sg sporia@sutd.edu.sg
Â... | {
"id": "1804.09301"
} |
2308.09583 | WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct | Large language models (LLMs), such as GPT-4, have shown remarkable
performance in natural language processing (NLP) tasks, including challenging
mathematical reasoning. However, most existing open-source models are only
pre-trained on large-scale internet data and without math-related optimization.
In this paper, we pr... | http://arxiv.org/pdf/2308.09583 | Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, Jianguang Lou, Chongyang Tao, Xiubo Geng, Qingwei Lin, Shifeng Chen, Dongmei Zhang | cs.CL, cs.AI, cs.LG | LLM, Mathematical Reasoning | null | cs.CL | 20230818 | 20230818 | 3 2 0 2
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# WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
# Haipeng Luo2â Qingfeng Sun1â Can Xu1â Pu Zhao1 Jianguang Lou1 Chongyang Tao1 Xiubo Geng1 Qingwei Lin1 Shifeng Chen2â Dongmei Zhang1
# 1Micr... | {
"id": "2302.13971"
} |
2308.08833 | CMB: A Comprehensive Medical Benchmark in Chinese | Large Language Models (LLMs) provide a possibility to make a great
breakthrough in medicine. The establishment of a standardized medical benchmark
becomes a fundamental cornerstone to measure progression. However, medical
environments in different regions have their local characteristics, e.g., the
ubiquity and signifi... | http://arxiv.org/pdf/2308.08833 | Xidong Wang, Guiming Hardy Chen, Dingjie Song, Zhiyi Zhang, Zhihong Chen, Qingying Xiao, Feng Jiang, Jianquan Li, Xiang Wan, Benyou Wang, Haizhou Li | cs.CL, cs.AI | null | null | cs.CL | 20230817 | 20230817 | 3 2 0 2
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# CMB: A Comprehensive Medical Benchmark in Chinese
# Xidong Wangâ , Guiming Hardy Chenâ , Dingjie Songâ , Zhiyi Zhangâ , Zhihong Chen, Qingying Xiao, Feng Jiang, Jianquan Li,
Xiang Wan, Benyou Wang , Haizhou Li
The Chinese University of Hong Kon... | {
"id": "2306.05685"
} |
2308.08285 | Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval | In this paper, we systematically study the potential of pre-training with
Large Language Model(LLM)-based document expansion for dense passage retrieval.
Concretely, we leverage the capabilities of LLMs for document expansion, i.e.
query generation, and effectively transfer expanded knowledge to retrievers
using pre-tr... | http://arxiv.org/pdf/2308.08285 | Guangyuan Ma, Xing Wu, Peng Wang, Zijia Lin, Songlin Hu | cs.IR, cs.CL | 10 pages, 3 tables, 4 figures, under review | null | cs.IR | 20230816 | 20230816 | 3 2 0 2
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# Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval
Guangyuan Ma1,2*, Xing Wu1,2*, Peng Wang1,2, Zijia Lin3, Songlin Hu1,2 1 Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China 2 S... | {
"id": "2203.05765"
} |
2308.08155 | AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation | AutoGen is an open-source framework that allows developers to build LLM
applications via multiple agents that can converse with each other to
accomplish tasks. AutoGen agents are customizable, conversable, and can operate
in various modes that employ combinations of LLMs, human inputs, and tools.
Using AutoGen, develop... | http://arxiv.org/pdf/2308.08155 | Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, Chi Wang | cs.AI, cs.CL | 43 pages (10 pages for the main text, 3 pages for references, and 30
pages for appendices) | null | cs.AI | 20230816 | 20231003 | 3 2 0 2
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# AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
Qingyun Wuâ , Gagan Bansalâ, Jieyu Zhang±, Yiran Wuâ , Beibin Liâ
Erkang Zhuâ, Li Jiangâ, Xiaoyun Zhangâ, Shaokun Zhangâ , Jiale Liuâ
Ahmed Awadallahâ, Ryen W. W... | {
"id": "2103.03874"
} |
2308.08493 | Time Travel in LLMs: Tracing Data Contamination in Large Language Models | Data contamination, i.e., the presence of test data from downstream tasks in
the training data of large language models (LLMs), is a potential major issue
in measuring LLMs' real effectiveness on other tasks. We propose a
straightforward yet effective method for identifying data contamination within
LLMs. At its core, ... | http://arxiv.org/pdf/2308.08493 | Shahriar Golchin, Mihai Surdeanu | cs.CL, cs.AI, cs.CR, cs.LG | v2 preprint | null | cs.CL | 20230816 | 20231001 | 2023:
3 2 0 2
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# TIME TRAVEL IN LLMS: TRACING DATA CONTAMINATION IN LARGE LANGUAGE MODELS
Shahriar Golchin & Mihai Surdeanu Department of Computer Science University of Arizona Tucson, AZ, USA {golchin,msurdeanu}@arizona.edu
# ABSTR... | {
"id": "2110.14168"
} |
2308.07540 | CALYPSO: LLMs as Dungeon Masters' Assistants | The role of a Dungeon Master, or DM, in the game Dungeons & Dragons is to
perform multiple tasks simultaneously. The DM must digest information about the
game setting and monsters, synthesize scenes to present to other players, and
respond to the players' interactions with the scene. Doing all of these tasks
while main... | http://arxiv.org/pdf/2308.07540 | Andrew Zhu, Lara J. Martin, Andrew Head, Chris Callison-Burch | cs.CL, cs.HC | 11 pages, 4 figures. AIIDE 2023 | null | cs.CL | 20230815 | 20230815 | 3 2 0 2
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# CALYPSO: LLMs as Dungeon Mastersâ Assistants
Andrew Zhu1, Lara J. Martin2*, Andrew Head1, Chris Callison-Burch1 1University of Pennsylvania 2University of Maryland, Baltimore County {andrz, head, ccb}@seas.upenn.edu, laramar@umbc.edu
# Abstract
The... | {
"id": "1706.03762"
} |
2308.07201 | ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate | Text evaluation has historically posed significant challenges, often
demanding substantial labor and time cost. With the emergence of large language
models (LLMs), researchers have explored LLMs' potential as alternatives for
human evaluation. While these single-agent-based approaches show promise,
experimental results... | http://arxiv.org/pdf/2308.07201 | Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, Zhiyuan Liu | cs.CL | null | null | cs.CL | 20230814 | 20230814 | 3 2 0 2
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# CHATEVAL: TOWARDS BETTER LLM-BASED EVALUA- TORS THROUGH MULTI-AGENT DEBATE
Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Zhiyuan Liuâ Department of Computer Science and Technology Tsinghua University zorowin123@gmail.com
Shanghang Zhang Peking... | {
"id": "2303.04048"
} |
2308.07124 | OctoPack: Instruction Tuning Code Large Language Models | Finetuning large language models (LLMs) on instructions leads to vast
performance improvements on natural language tasks. We apply instruction tuning
using code, leveraging the natural structure of Git commits, which pair code
changes with human instructions. We compile CommitPack: 4 terabytes of Git
commits across 350... | http://arxiv.org/pdf/2308.07124 | Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, Shayne Longpre | cs.CL, cs.AI | 57 pages (9 main), 39 figures, 16 tables | null | cs.CL | 20230814 | 20230814 | 3 2 0 2
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# OCTOPACK: INSTRUCTION TUNING CODE LARGE LANGUAGE MODELS
Niklas Muennighoff Qian Liu Armel Zebaze Qinkai Zheng Binyuan Hui Terry Yue Zhuo Swayam Singh Xiangru Tang Leandro von Werra Shayne Longpre n.muennighoff@gmail.com
# ABSTRACT
Finetuning large la... | {
"id": "2302.00288"
} |
2308.07107 | Large Language Models for Information Retrieval: A Survey | As a primary means of information acquisition, information retrieval (IR)
systems, such as search engines, have integrated themselves into our daily
lives. These systems also serve as components of dialogue, question-answering,
and recommender systems. The trajectory of IR has evolved dynamically from its
origins in te... | http://arxiv.org/pdf/2308.07107 | Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Haonan Chen, Zhicheng Dou, Ji-Rong Wen | cs.CL, cs.IR | updated to version 2 | null | cs.CL | 20230814 | 20240119 | 4 2 0 2 n a J 9 1 ] L C . s c [
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# Large Language Models for Information Retrieval: A Survey
Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Haonan Chen, Zhicheng Dou, and Ji-Rong Wen
AbstractâAs a primary means of information acquisition, information... | {
"id": "2305.03195"
} |
2308.06921 | CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes | Computing educators face significant challenges in providing timely support
to students, especially in large class settings. Large language models (LLMs)
have emerged recently and show great promise for providing on-demand help at a
large scale, but there are concerns that students may over-rely on the outputs
produced... | http://arxiv.org/pdf/2308.06921 | Mark Liffiton, Brad Sheese, Jaromir Savelka, Paul Denny | cs.CY | null | null | cs.CY | 20230814 | 20230814 | 3 2 0 2
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# CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes
# Mark Liffiton mliffito@iwu.edu Illinois Wesleyan University Bloomington, Illinois, USA
Brad Sheese bsheese@iwu.edu Illinois Wesleyan University Bloomi... | {
"id": "2304.03938"
} |
2308.06782 | PentestGPT: An LLM-empowered Automatic Penetration Testing Tool | Penetration testing, a crucial industrial practice for ensuring system
security, has traditionally resisted automation due to the extensive expertise
required by human professionals. Large Language Models (LLMs) have shown
significant advancements in various domains, and their emergent abilities
suggest their potential... | http://arxiv.org/pdf/2308.06782 | Gelei Deng, Yi Liu, Víctor Mayoral-Vilches, Peng Liu, Yuekang Li, Yuan Xu, Tianwei Zhang, Yang Liu, Martin Pinzger, Stefan Rass | cs.SE, cs.CR | null | null | cs.SE | 20230813 | 20230813 | 3 2 0 2
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# PENTESTGPT: An LLM-empowered Automatic Penetration Testing Tool
Gelei Deng1, Yi Liu1, V´ıctor Mayoral-Vilches2,3 , Peng Liu4, Yuekang Li5, Yuan Xu 1, Tianwei Zhang1, Yang Liu1, Martin Pinzger2, and Stefan Rass6
1Nanyang Technological University, 2A... | {
"id": "2305.13860"
} |
2308.05960 | BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents | The massive successes of large language models (LLMs) encourage the emerging
exploration of LLM-augmented Autonomous Agents (LAAs). An LAA is able to
generate actions with its core LLM and interact with environments, which
facilitates the ability to resolve complex tasks by conditioning on past
interactions such as obs... | http://arxiv.org/pdf/2308.05960 | Zhiwei Liu, Weiran Yao, Jianguo Zhang, Le Xue, Shelby Heinecke, Rithesh Murthy, Yihao Feng, Zeyuan Chen, Juan Carlos Niebles, Devansh Arpit, Ran Xu, Phil Mui, Huan Wang, Caiming Xiong, Silvio Savarese | cs.AI | Preprint | null | cs.AI | 20230811 | 20230811 | 3 2 0 2
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PREPRINT
# BOLAA: BENCHMARKING AND ORCHESTRATING LLM-AUGMENTED AUTONOMOUS AGENTS
Zhiwei Liuâ â, Weiran Yaoâ , Jianguo Zhangâ , Le Xueâ , Shelby Heineckeâ , Rithesh Murthyâ , Yihao Fengâ , Zeyuan Chenâ , Juan Carlos Nieblesâ , Devansh Arpitâ... | {
"id": "2204.02311"
} |
2308.06391 | Dynamic Planning with a LLM | While Large Language Models (LLMs) can solve many NLP tasks in zero-shot
settings, applications involving embodied agents remain problematic. In
particular, complex plans that require multi-step reasoning become difficult
and too costly as the context window grows. Planning requires understanding the
likely effects of ... | http://arxiv.org/pdf/2308.06391 | Gautier Dagan, Frank Keller, Alex Lascarides | cs.CL, cs.RO | null | null | cs.CL | 20230811 | 20230811 | 3 2 0 2
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# Dynamic Planning with a LLM
# Frank Keller School of Informatics University of Edinburgh, UK gautier.dagan@ed.ac.uk, {keller, alex}@inf.ed.ac.uk
# Abstract
While Large Language Models (LLMs) can solve many NLP tasks in zero-shot settings, ap- plicati... | {
"id": "2303.11366"
} |
2308.06394 | Detecting and Preventing Hallucinations in Large Vision Language Models | Instruction tuned Large Vision Language Models (LVLMs) have significantly
advanced in generalizing across a diverse set of multi-modal tasks, especially
for Visual Question Answering (VQA). However, generating detailed responses
that are visually grounded is still a challenging task for these models. We
find that even ... | http://arxiv.org/pdf/2308.06394 | Anisha Gunjal, Jihan Yin, Erhan Bas | cs.CV, cs.LG | preprint | null | cs.CV | 20230811 | 20230818 | 3 2 0 2
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# Detecting and Preventing Hallucinations in Large Vision Language Models
# Anisha Gunjal*, Jihan Yin*, Erhan Basâ Scale AI {anisha.gunjal,jihan.yin,erhan.bas}@scale.com
# Abstract
Instruction tuned Large Vision Language Models (LVLMs) have significa... | {
"id": "2302.04023"
} |
2308.05696 | A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment | Training large language models (LLMs) with open-domain instruction data has
yielded remarkable success in aligning to end tasks and user preferences.
Extensive research has highlighted that enhancing the quality and diversity of
instruction data consistently improves performance. However, the impact of data
complexity,... | http://arxiv.org/pdf/2308.05696 | Yingxiu Zhao, Bowen Yu, Binyuan Hui, Haiyang Yu, Fei Huang, Yongbin Li, Nevin L. Zhang | cs.CL | null | null | cs.CL | 20230810 | 20230810 | 3 2 0 2
g u A 0 1 ] L C . s c [
1 v 6 9 6 5 0 . 8 0 3 2 : v i X r a
# A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment
Yingxiu Zhao1, Bowen Yu2â, Binyuan Hui2, Haiyang Yu2, Fei Huang2, Yongbin Li2â, Nevin L. Zhang1 1 The Hong Kong University of Science and Technology, 2 Alibaba Gro... | {
"id": "2307.12966"
} |
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