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Peixin Cao

2 accepted papers

2024

Adversarial Preference Optimization: Enhancing Your Alignment via RM-LLM Game

ACL 2024findings

Human preference alignment is essential to improve the interaction quality of large language models (LLMs). Existing alignment methods depend on manually annotated preference data to guide the LLM optimization directions. However, continuously updating LLMs for alignment raises a distribution gap be…

2024

Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models

EMNLP 2024main

Retrieval-augmented language model (RALM) represents a significant advancement in mitigating factual hallucination by leveraging external knowledge sources. However, the reliability of the retrieved information is not always guaranteed, and the retrieval of irrelevant data can mislead the response g…

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