21 citations · 57 across the 20 of their papers we have counts for
6 papers · 1 filter
Competition-Level Problems are Effective LLM Evaluators
Yiming Huang, Zhenghao Lin, Xiao Liu +8
Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about these abilities and the potential data contamination problem rec…
Language Models can be Logical Solvers
Jiazhan Feng, Ruochen Xu, Junheng Hao +4
Logical reasoning is a fundamental aspect of human intelligence and a key component of tasks like problem-solving and decision-making. Recent advancements have enabled Large Langua…
Adapting LLM Agents with Universal Feedback in Communication
Kuan Wang, Yadong Lu, Michael Santacroce +3
Recent advances in large language models (LLMs) have demonstrated potential for LLM agents. To facilitate the training for these agents with both linguistic feedback and non-lingui…
An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models
Yadong Lu, Chunyuan Li, Haotian Liu +3
Visual instruction tuning has recently shown encouraging progress with open-source large multimodal models (LMM) such as LLaVA and MiniGPT-4. However, most existing studies of open…
ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving
Zhibin Gou, Zhihong Shao, Yeyun Gong +5
Large language models have made significant progress in various language tasks, yet they still struggle with complex mathematics. In this paper, we propose ToRA a series of Tool-in…
Efficient RLHF: Reducing the Memory Usage of PPO
Michael Santacroce, Yadong Lu, Han Yu +2
Reinforcement Learning with Human Feedback (RLHF) has revolutionized language modeling by aligning models with human preferences. However, the RL stage, Proximal Policy Optimizatio…