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.

翻译：摘要：摘要是大型语言模型（LLM）的一项重要应用。以往对摘要模型的评估大多聚焦于其在内容选择、语法正确性和连贯性方面的表现。然而，众所周知，LLM会复制并强化有害的社会偏见。这引出一个问题：这些偏见是否会影响模型在相对受约束的任务（如摘要）中的输出？为回答这一问题，我们首先提出并定义摘要模型中偏见行为的若干概念，并配套给出量化这些偏见的实用方法。由于我们发现输入文档固有的偏见会干扰分析，我们进一步提出一种方法，通过精细控制人口统计学属性来生成输入文档。这使我们能规避该问题，同时仍能处理接近真实的输入文档。最后，我们将所提方法应用于专用摘要模型和通用对话模型生成的摘要中。研究发现，单文档摘要中的内容选择似乎基本不受偏见影响，而幻觉现象则显示出偏见传播至生成摘要的证据。