Papers by Zishu Qin

1 papers
Instance-level Randomization: Toward More Stable LLM Evaluations (2025.findings-emnlp)

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Challenge: Evaluations of large language models suffer from instability, where small changes of random factors can lead to drastic fluctuations of scores and even model rankings.
Approach: They propose an instance-level randomization method to reduce variance and improve fairness in evaluations by randomizing all factors that affect evaluation scores for every single instance.
Outcome: The proposed method reduces variance and improves fairness in model comparisons by using instance-level randomization.

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