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English(EN) Finding the Signal in the Spam: Jointly Learning Rewards and Worker Reliability from Pairwise Comparisons

新算法从比较中学习工人可靠性和项目奖励

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新算法，可以从成对比较中同时学习项目奖励和工人可靠性，尤其是在众包环境中。所提出的方法利用了玻尔兹曼理性模型，通过纳入工人能力来扩展 Bradley-Terry-Luce 模型。使用 Polya-Gamma 潜在变量推导了一个基于 EM 的算法，将问题转化为具有理论收敛保证的矩阵传感任务。在合成数据和真实世界数据上的实验表明，该算法对不可靠和对抗性工人具有鲁棒性，并且优于现有基线。 AI

影响 这项研究通过更好地考虑不可靠的人工标注者，有可能提高用于训练 AI 模型的数据质量。

排序理由 该集群包含一篇学术论文，详细介绍了一种从成对比较中学习的新算法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新算法从比较中学习工人可靠性和项目奖励

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该集群包含一篇学术论文，详细介绍了一种从成对比较中学习的新算法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kaustubh Shivshankar Shejole, Tanish Agarwal, Arpit Agarwal, Avishek Ghosh ·

    从成对比较中联合学习奖励和工人可靠性：在垃圾邮件中寻找信号

    arXiv:2608.10045v1 Announce Type: cross Abstract: The problem of learning from pairwise comparisons has been widely studied across many domains such as recommendation systems, social choice, and more recently, fine-tuning large language models. In this problem, the goal is to lea…

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