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EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction
We proposes EASYTOOL, a method that simplifies tool documentation into concise instructions, improving tool use by language models.
Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Yongliang Shen, Ren Kan, Dongsheng Li, Deqing Yang
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EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction
EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms
We introduce EvoAgent, a method using evolutionary algorithms to automatically expand expert agents into multi-agent systems, enhancing the task-solving capabilities of large language model-based agents without additional human design.
Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Dongsheng Li, Deqing Yang
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EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms
Revealing the Barriers of Language Agents in Planning
We reveal the two key factors that hinder language agents from achieving human-level planning.
Jian Xie, Kexun Zhang, Jiangjie Chen, Siyu Yuan, Kai Zhang, Yikai Zhang, Lei Li, Yanghua Xiao
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Revealing the Barriers of Language Agents in Planning
SelfGoal: Your Language Agents Already Know How to Achieve High-level Goals
We introduce SelfGoal, an automatic approach that enhances language agents’ capabilities to achieve high-level goals with limited instructions and delayed feedback by adaptively breaking down goals into practical subgoals.
Ruihan Yang, Jiangjie Chen, Yikai Zhang, Siyu Yuan, Aili Chen, Kyle Richardson, Yanghua Xiao, Deqing Yang
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SelfGoal: Your Language Agents Already Know How to Achieve High-level Goals
SEGMENT+: Long Text Processing with Short-Context Language Models
We introduce SEGMENT+, a framework that enables LMs to efficiently handle extended inputs within limited context windows, improving performance in long-document tasks through structured notes and a filtering module.
Wei Shi, Shuang Li, Kerun Yu, Jinglei Chen, Zujie Liang, Xinhui Wu, Yuxi Qian, Feng Wei, Bo Zheng, Jiaqing Liang, Jiangjie Chen, Yanghua Xiao
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SEGMENT+: Long Text Processing with Short-Context Language Models
How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?
We study how LLMs handle irrelevant information and find they struggle with content that is semantically related but ultimately not pertinent, highlighting the limitations of current systems in filtering out such distractions.
Siye Wu, Jian Xie, Jiangjie Chen, Tinghui Zhu, Kai Zhang, Yanghua Xiao
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How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?
DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?
We introduce DetectBench, a benchmark for testing LLMs’ evidence detection in long contexts, and demonstrates that while existing LLMs lag behind human performance, the proposed Detective Reasoning Prompt and Finetuning methods can significantly improve their evidence detection and reasoning capabilities.
Zhouhong Gu, Lin Zhang, Xiaoxuan Zhu, Jiangjie Chen, Wenhao Huang, Yikai Zhang, Shusen Wang, Zheyu Ye, Yan Gao, Hongwei Feng, Yanghua Xiao
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DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?
Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works
We propose evaluating large language models’ character understanding through character profiling, using the CroSS dataset and showing promising results for role-playing agent development.
Xinfeng Yuan, Siyu Yuan, Yuhan Cui, Tianhe Lin, Xintao Wang, Rui Xu, Jiangjie Chen, Deqing Yang
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Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works
TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation
TimeArena enhances LLMs with temporal dynamics for better multitasking, showing advanced models like GPT-4 still trail behind human temporal awareness.
Yikai Zhang, Siyu Yuan, Caiyu Hu, Kyle Richardson, Yanghua Xiao, Jiangjie Chen
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TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews
We propose InCharacter, a method using psychological scales to evaluate the personality fidelity of role-playing agents (RPAs) powered by large language models.
Xintao Wang, Yunze Xiao, Jen-Tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao
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InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews