5 papers
Quantifying and Understanding Uncertainty in Large Reasoning Models
Yangyi Li, Chenxu Zhao, Mengdi Huai
Large Reasoning Models (LRMs) have recently demonstrated significant improvements in complex reasoning. While quantifying generation uncertainty in LRMs is crucial, traditional met…
Search, Do not Guess: Teaching Small Language Models to Be Effective Search Agents
Yizhou Liu, Qi Sun, Yulin Chen +2
Agents equipped with search tools have emerged as effective solutions for knowledge-intensive tasks. While Large Language Models (LLMs) exhibit strong reasoning capabilities, their…
Towards Unveiling Vulnerabilities of Large Reasoning Models in Machine Unlearning
Aobo Chen, Chenxu Zhao, Chenglin Miao +1
Large language models (LLMs) possess strong semantic understanding, driving significant progress in data mining applications. This is further enhanced by large reasoning models (LR…
Reasoning Models Know When They're Right: Probing Hidden States for Self-Verification
Anqi Zhang, Yulin Chen, Jane Pan +4
Reasoning models have achieved remarkable performance on tasks like math and logical reasoning thanks to their ability to search during reasoning. However, they still suffer from o…
Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks World
Guande Wu, Chen Zhao, Claudio Silva +1
Language agents that interact with the world on their own have great potential for automating digital tasks. While large language model (LLM) agents have made progress in understan…