3 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
TellOnce: Correcting Coding Agents Once, Improving Their Future Actions
Yujun Zhou, Kehan Guo, Haomin Zhuang +8
Interactive LLM agents increasingly work with users across tasks, yet remembering a previous correction does not ensure that the agent changes its behavior in the future. In a diag…
Evolving Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation
Yujun Zhou, Zhenwen Liang, Haolin Liu +7
Large language models (LLMs) can improve through reinforcement learning with verifiable rewards (RLVR), but many settings lack reference answers or external verifiers. Label-free s…
Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification
Zhenwen Liang, Ye Liu, Tong Niu +3
Despite significant advancements in the general capability of large language models (LLMs), they continue to struggle with consistent and accurate reasoning, especially in complex…
Defending Jailbreak Prompts via In-Context Adversarial Game
Yujun Zhou, Yufei Han, Haomin Zhuang +4
Large Language Models (LLMs) demonstrate remarkable capabilities across diverse applications. However, concerns regarding their security, particularly the vulnerability to jailbrea…
Manipulating Predictions over Discrete Inputs in Machine Teaching
Xiaodong Wu, Yufei Han, Hayssam Dahrouj +3
Machine teaching often involves the creation of an optimal (typically minimal) dataset to help a model (referred to as the `student') achieve specific goals given by a teacher. Whi…