The variations between in-group and out-group speech (intergroup bias) are
subtle and could underlie many social phenomena like stereotype perpetuation
and implicit bias. In this paper, we model the intergroup bias as a tagging
task on English sports comments from forums dedicated to fandom for NFL teams.
We curate a unique dataset of over 6 million game-time comments from opposing
perspectives (the teams in the game), each comment grounded in a non-linguistic
description of the events that precipitated these comments (live win
probabilities for each team). Expert and crowd annotations justify modeling the
bias through tagging of implicit and explicit referring expressions and reveal
the rich, contextual understanding of language and the world required for this
task. For large-scale analysis of intergroup variation, we use LLMs for
automated tagging, and discover that some LLMs perform best when prompted with
linguistic descriptions of the win probability at the time of the comment,
rather than numerical probability. Further, large-scale tagging of comments
using LLMs uncovers linear variations in the form of referent across win
probabilities that distinguish in-group and out-group utterances. Code and data
are available at this https URL .

通过在英语体育评论中对隶属群体内外言论（群体偏见）的模型，本文揭示了在社会现象如刻板印象延续和隐性偏见中可能存在的微妙差异。为了大规模分析群体间变化，我们使用 LLMs 进行自动标记，并发现一些 LLMs 最佳工作方式是在评论时使用语言描述赢得概率，而不是数值概率。此外，使用 LLMs 对评论进行大规模标记揭示了在不同赢得概率下区别隶属群体内外言论形式的线性变化。