Papers by Mingzhang Yin

    1 papers
    Permutative Preference Alignment from Listwise Ranking of Human Judgments (2025.emnlp-main)

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    Challenge: Existing methods to align Large Language Models with human preferences are based on the Bradley-Terry model, but when multiple responses are available, the B-T model fails to guarantee an accurate list ranking of the responses.
    Approach: They propose an offline listwise approach that incorporates the Normalized Discounted Cumulative Gain (NDCG) as an alternative training objective for LLM alignment.
    Outcome: The proposed approach outperforms existing pairwise and listwise methods on evaluation sets and general benchmarks such as AlpacaEval.

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