86 citations · 611 across the 79 of their papers we have counts for
83 papers
H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons in LLMs
Cheng Gao, Huimin Chen, Chaojun Xiao +3
Large language models (LLMs) frequently generate hallucinations -- plausible but factually incorrect outputs -- undermining their reliability. While prior work has examined halluci…
Large Language Models' Complicit Responses to Illicit Instructions across Socio-Legal Contexts
Xing Wang, Huiyuan Xie, Yiyan Wang +7
Large language models (LLMs) are now deployed at unprecedented scale, assisting millions of users in daily tasks. However, the risk of these models assisting unlawful activities re…
A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules
Kairong Luo, Haodong Wen, Shengding Hu +5
Training large models is both resource-intensive and time-consuming, making it crucial to understand the quantitative relationship between model performance and hyperparameters. In…
ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer
Jinyi Hu, Shengding Hu, Yuxuan Song +6
Autoregressive and diffusion models have achieved remarkable progress in language models and visual generation, respectively. We present ACDiT, a novel Autoregressive blockwise Con…
Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs
Cheng Gao, Chaojun Xiao, Zhenghao Liu +3
Legal case retrieval (LCR) aims to provide similar cases as references for a given fact description. This task is crucial for promoting consistent judgments in similar cases, effec…
LEGENT: Open Platform for Embodied Agents
Zhili Cheng, Zhitong Wang, Jinyi Hu +7
Despite advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), their integration into language-grounded, human-like embodied agents remains incomplete, hi…
BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences
Ao Sun, Weilin Zhao, Xu Han +4
Effective attention modules have played a crucial role in the success of Transformer-based large language models (LLMs), but the quadratic time and memory complexities of these att…
Unified View of Grokking, Double Descent and Emergent Abilities: A Perspective from Circuits Competition
Yufei Huang, Shengding Hu, Xu Han +2
Recent studies have uncovered intriguing phenomena in deep learning, such as grokking, double descent, and emergent abilities in large language models, which challenge human intuit…
Bench: Extending Long Context Evaluation Beyond 100K Tokens
Xinrong Zhang, Yingfa Chen, Shengding Hu +8
Processing and reasoning over long contexts is crucial for many practical applications of Large Language Models (LLMs), such as document comprehension and agent construction. Despi…
UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset
Haoyu Wang, Shuo Wang, Yukun Yan +9
Open-source large language models (LLMs) have gained significant strength across diverse fields. Nevertheless, the majority of studies primarily concentrate on English, with only l…
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents
Cheng Qian, Bingxiang He, Zhong Zhuang +8
Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. Although adep…
Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication
Weize Chen, Chenfei Yuan, Jiarui Yuan +6
Natural language (NL) has long been the predominant format for human cognition and communication, and by extension, has been similarly pivotal in the development and application of…
OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems
Chaoqun He, Renjie Luo, Yuzhuo Bai +11
Recent advancements have seen Large Language Models (LLMs) and Large Multimodal Models (LMMs) surpassing general human capabilities in various tasks, approaching the proficiency le…
ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models
Chenyang Song, Xu Han, Zhengyan Zhang +8
Activation sparsity refers to the existence of considerable weakly-contributed elements among activation outputs. As a prevalent property of the models using the ReLU activation fu…
InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory
Chaojun Xiao, Pengle Zhang, Xu Han +5
Large language models (LLMs) have emerged as a cornerstone in real-world applications with lengthy streaming inputs (e.g., LLM-driven agents). However, existing LLMs, pre-trained o…
UniMem: Towards a Unified View of Long-Context Large Language Models
Junjie Fang, Likai Tang, Hongzhe Bi +12
Long-context processing is a critical ability that constrains the applicability of large language models (LLMs). Although there exist various methods devoted to enhancing the long-…
Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution
Cheng Qian, Shihao Liang, Yujia Qin +6
This paper introduces Investigate-Consolidate-Exploit (ICE), a novel strategy for enhancing the adaptability and flexibility of AI agents through inter-task self-evolution. Unlike…
DebugBench: Evaluating Debugging Capability of Large Language Models
Runchu Tian, Yining Ye, Yujia Qin +8
Large Language Models (LLMs) have demonstrated exceptional coding capability. However, as another critical component of programming proficiency, the debugging capability of LLMs re…
NExT-Chat: An LMM for Chat, Detection and Segmentation
Ao Zhang, Yuan Yao, Wei Ji +2
The development of large language models (LLMs) has greatly advanced the field of multimodal understanding, leading to the emergence of large multimodal models (LMMs). In order to…
Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub
Bohan Lyu, Xin Cong, Heyang Yu +9
Large Language Models (LLMs) excel in traditional natural language processing tasks but struggle with problems that require complex domain-specific calculations or simulations. Whi…
Experiential Co-Learning of Software-Developing Agents
Chen Qian, Yufan Dang, Jiahao Li +9
Recent advancements in large language models (LLMs) have brought significant changes to various domains, especially through LLM-driven autonomous agents. A representative scenario…
RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback
Tianyu Yu, Yuan Yao, Haoye Zhang +8
Multimodal Large Language Models (MLLMs) have recently demonstrated impressive capabilities in multimodal understanding, reasoning, and interaction. However, existing MLLMs prevale…
ProAgent: From Robotic Process Automation to Agentic Process Automation
Yining Ye, Xin Cong, Shizuo Tian +9
From ancient water wheels to robotic process automation (RPA), automation technology has evolved throughout history to liberate human beings from arduous tasks. Yet, RPA struggles…
MUSER: A Multi-View Similar Case Retrieval Dataset
Qingquan Li, Yiran Hu, Feng Yao +4
Similar case retrieval (SCR) is a representative legal AI application that plays a pivotal role in promoting judicial fairness. However, existing SCR datasets only focus on the fac…