Summarization is an important application of large language models (LLMs). Most previous evaluation of summarization models has focused on their performance in content selection, grammaticality and coherence. However, it is well known that LLMs reproduce and reinforce harmful social biases. This raises the question: Do these biases affect model outputs in a relatively constrained setting like summarization? To help answer this question, we first motivate and introduce a number of definitions for biased behaviours in summarization models, along with practical measures to quantify them. Since we find biases inherent to the input document can confound our analysis, we additionally propose a method to generate input documents with carefully controlled demographic attributes. This allows us to sidestep this issue, while still working with somewhat realistic input documents. Finally, we apply our measures to summaries generated by both purpose-built summarization models and general purpose chat models. We find that content selection in single document summarization seems to be largely unaffected by bias, while hallucinations exhibit evidence of biases propagating to generated summaries.

概括是大型语言模型（LLMs）的一个重要应用，先前的评估主要关注它们在内容选择、语法和连贯性方面的表现，但众所周知LLMs会传递和强化有害的社会偏见，因此我们需要研究这些偏见是否会影响文摘模型的输出。为了回答这个问题，我们首先提出了一些针对文摘模型中的偏见行为的定义，并介绍了实际的度量方法。然后，为了避免输入文档中的内在偏见对分析的干扰，我们提出了一种方法来生成具有精细控制的人口属性的输入文档。最后，我们将我们的度量方法应用于专为文摘模型和通用聊天模型生成的文摘中，发现单一文档的内容选择似乎在很大程度上不受偏见的影响，而虚构现象显示出有偏见传播到生成的文摘中的证据。

调查新闻摘要中的性别偏见