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cs.CL2024
It Takes Two: On the Seamlessness between Reward and Policy Model in RLHF
Taiming Lu, Lingfeng Shen, Xinyu Yang +3
Reinforcement Learning from Human Feedback (RLHF) involves training policy models (PMs) and reward models (RMs) to align language models with human preferences. Instead of focusing…
cs.CL2024★ 1 cited
Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell
Taiming Lu, Muhan Gao, Kuai Yu +2
Large Language Models (LLMs) exhibit positional bias, struggling to utilize information from the middle or end of long contexts. Our study explores LLMs' long-context reasoning by…
cs.CL2024★ 1 cited
Learn and Unlearn: Addressing Misinformation in Multilingual LLMs
Taiming Lu, Philipp Koehn
This paper investigates the propagation of harmful information in multilingual large language models (LLMs) and evaluates the efficacy of various unlearning methods. We demonstrate…