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cs.CL2024
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…
cs.CL2024
A Troublemaker with Contagious Jailbreak Makes Chaos in Honest Towns
Tianyi Men, Pengfei Cao, Zhuoran Jin +3
With the development of large language models, they are widely used as agents in various fields. A key component of agents is memory, which stores vital information but is suscepti…
cs.CL2024
Unlocking the Future: Exploring Look-Ahead Planning Mechanistic Interpretability in Large Language Models
Tianyi Men, Pengfei Cao, Zhuoran Jin +3
Planning, as the core module of agents, is crucial in various fields such as embodied agents, web navigation, and tool using. With the development of large language models (LLMs),…