14 citations · 16 across the 14 of their papers we have counts for
11 papers · 1 filter
SFT Conflicts, RL Coexists: A Theoretical and Empirical Analysis of Multi-Task Learning for LLMs
Kejian Zhu, Zhuoran Jin, Shangqing Tu +5
Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) exhibit fundamentally different behaviors in enhancing multi-task reasoning for large language models (LLMs). Our preli…
Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do
Zhuoran Jin, Kejian Zhu, Hongbang Yuan +5
Chain-of-Thought (CoT) has become a standard method for improving reasoning capabilities in large language models (LLMs) by eliciting step-by-step thinking, but its effectiveness i…
Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application
Jiachun Li, Zhuoran Jin, Tianyi Men +12
Environments serve as interactive systems for large language model (LLM) based agents across diverse scenarios and play a crucial role in driving the continual evolution of model c…
Omni-Reward: Towards Generalist Omni-Modal Reward Modeling with Free-Form Preferences
Zhuoran Jin, Hongbang Yuan, Kejian Zhu +5
Reward models (RMs) play a critical role in aligning AI behaviors with human preferences, yet they face two fundamental challenges: (1) Modality Imbalance, where most RMs are mainl…
RULE: Reinforcement UnLEarning Achieves Forget-Retain Pareto Optimality
Chenlong Zhang, Zhuoran Jin, Hongbang Yuan +5
The widespread deployment of Large Language Models (LLMs) trained on massive, uncurated corpora has raised growing concerns about the inclusion of sensitive, copyrighted, or illega…
RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment
Zhuoran Jin, Hongbang Yuan, Tianyi Men +4
Despite the significant progress made by existing retrieval augmented language models (RALMs) in providing trustworthy responses and grounding in reliable sources, they often overl…
Towards Robust Knowledge Unlearning: An Adversarial Framework for Assessing and Improving Unlearning Robustness in Large Language Models
Hongbang Yuan, Zhuoran Jin, Pengfei Cao +3
LLM have achieved success in many fields but still troubled by problematic content in the training corpora. LLM unlearning aims at reducing their influence and avoid undesirable be…
Beyond Under-Alignment: Atomic Preference Enhanced Factuality Tuning for Large Language Models
Hongbang Yuan, Yubo Chen, Pengfei Cao +3
Large language models (LLMs) have achieved remarkable success but still tend to generate factually erroneous responses, a phenomenon known as hallucination. A recent trend is to us…
RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models
Zhuoran Jin, Pengfei Cao, Chenhao Wang +6
Large language models (LLMs) inevitably memorize sensitive, copyrighted, and harmful knowledge from the training corpus; therefore, it is crucial to erase this knowledge from the m…
Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Language Models
Hongbang Yuan, Pengfei Cao, Zhuoran Jin +4
Large Language Models (LLMs) have shown impressive capabilities but still suffer from the issue of hallucinations. A significant type of this issue is the false premise hallucinati…
Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models
Zhuoran Jin, Pengfei Cao, Hongbang Yuan +6
Recently, retrieval augmentation and tool augmentation have demonstrated a remarkable capability to expand the internal memory boundaries of language models (LMs) by providing exte…