5 citations · 5 across the 3 of their papers we have counts for
13 papers · 1 filter
Lessons from the Trenches on Reproducible Evaluation of Language Models
Stella Biderman, Hailey Schoelkopf, Lintang Sutawika +27
Reliable evaluation of language models (LMs) remains an open challenge. Re- searchers and engineers face methodological issues such as the sensitivity of models to evaluation setup…
Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving
Xiangru Tang, Tianrui Qin, Tianhao Peng +15
AI agent frameworks operate in isolation, forcing agents to rediscover solutions and repeat mistakes across different systems. Despite valuable problem-solving experiences accumula…
ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning
Xiangru Tang, Tianyu Hu, Muyang Ye +9
Chemical reasoning usually involves complex, multi-step processes that demand precise calculations, where even minor errors can lead to cascading failures. Furthermore, large langu…
FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial Documents
Yilun Zhao, Yitao Long, Yuru Jiang +7
We introduce FinDVer, a comprehensive benchmark specifically designed to evaluate the explainable claim verification capabilities of LLMs in the context of understanding and analyz…
ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code
Xiangru Tang, Yuliang Liu, Zefan Cai +21
Despite Large Language Models (LLMs) like GPT-4 achieving impressive results in function-level code generation, they struggle with repository-scale code understanding (e.g., coming…
Step-Back Profiling: Distilling User History for Personalized Scientific Writing
Xiangru Tang, Xingyao Zhang, Yanjun Shao +6
Large language models (LLM) excel at a variety of natural language processing tasks, yet they struggle to generate personalized content for individuals, particularly in real-world…