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

72 citations · 107 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

SimSD: Simple Speculative Decoding in Diffusion Language Models

Junxia Cui, Haotian Ye, Runchu Tian +9

Diffusion large language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs, offering faster inference through parallel or blockwise decodi…

cs.LG2026

Cog-DRIFT: Exploration on Adaptively Reformulated Instances Enables Learning from Hard Reasoning Problems

Justin Chih-Yao Chen, Archiki Prasad, Zaid Khan +4

Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning abilities of LLMs, yet a fundamental limitation remains: models cannot learn from problems that are…

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.SE2024★ 2 cited

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…

cs.AI2023★ 72 cited

ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

Yujia Qin, Shihao Liang, Yining Ye +16

Despite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to…

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,…