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20232026
most citedToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

72 citations · 211 across the 32 of their papers we have counts for

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14 papers · 1 filter

cs.CL2025

MiniCPM4: Ultra-Efficient LLMs on End Devices

MiniCPM Team, Chaojun Xiao, Yuxuan Li +80

This paper introduces MiniCPM4, a highly efficient large language model (LLM) designed explicitly for end-side devices. We achieve this efficiency through systematic innovation in…

cs.CL2025

Synergizing Large Language Models and Theory for Human-like Causal Reasoning

Yanxi Zhang, Xin Cong, Zhong Zhang +3

Performing causal reasoning akin to humans is fundamental to achieving strong artificial intelligence. However, existing studies on causal reasoning and large language models (LLMs…

cs.CL2025★ 1 cited

Learning to Generate Structured Output with Schema Reinforcement Learning

Yaxi Lu, Haolun Li, Xin Cong +6

This study investigates the structured generation capabilities of large language models (LLMs), focusing on producing valid JSON outputs against a given schema. Despite the widespr…

cs.CL2025

AgentRM: Enhancing Agent Generalization with Reward Modeling

Yu Xia, Jingru Fan, Weize Chen +7

Existing LLM-based agents have achieved strong performance on held-in tasks, but their generalizability to unseen tasks remains poor. Hence, some recent work focus on fine-tuning t…

cs.CL2024

Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs

Runchu Tian, Yanghao Li, Yuepeng Fu +10

Positional bias in large language models (LLMs) hinders their ability to effectively process long inputs. A prominent example is the "lost in the middle" phenomenon, where LLMs str…

cs.CL2024

Learning Evolving Tools for Large Language Models

Guoxin Chen, Zhong Zhang, Xin Cong +5

Tool learning enables large language models (LLMs) to interact with external tools and APIs, greatly expanding the application scope of LLMs. However, due to the dynamic nature of…

cs.CL2024

MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization

Zhiyu Yang, Zihan Zhou, Shuo Wang +10

Scientific data visualization plays a crucial role in research by enabling the direct display of complex information and assisting researchers in identifying implicit patterns. Des…

cs.CL2024★ 1 cited

RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation

Qinyu Luo, Yining Ye, Shihao Liang +10

Generative models have demonstrated considerable potential in software engineering, particularly in tasks such as code generation and debugging. However, their utilization in the d…

cs.CL2024★ 4 cited

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…

cs.CL2024

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-…

cs.CL2024★ 7 cited

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…

cs.CL2023★ 2 cited

Rational Decision-Making Agent with Internalized Utility Judgment

Yining Ye, Xin Cong, Shizuo Tian +5

Large language models (LLMs) have demonstrated remarkable advancements and have attracted significant efforts to develop LLMs into agents capable of executing intricate multi-step…

cs.CL2023

Exploring Format Consistency for Instruction Tuning

Shihao Liang, Runchu Tian, Kunlun Zhu +6

Instruction tuning has emerged as a promising approach to enhancing large language models in following human instructions. It is shown that increasing the diversity and number of i…

cs.CL2023★ 33 cited

Tool Learning with Foundation Models

Yujia Qin, Shengding Hu, Yankai Lin +38

Humans possess an extraordinary ability to create and utilize tools, allowing them to overcome physical limitations and explore new frontiers. With the advent of foundation models,…