One way to personalize chatbot interactions is by establishing common ground with the intended reader. A domain where establishing mutual understanding could be particularly impactful is vaccine concerns and misinformation. Vaccine interventions are forms of messaging which aim to answer concerns expressed about vaccination. Tailoring responses in this domain is difficult, since opinions often have seemingly little ideological overlap. We define the task of tailoring vaccine interventions to a Common-Ground Opinion (CGO). Tailoring responses to a CGO involves meaningfully improving the answer by relating it to an opinion or belief the reader holds. In this paper we introduce TAILOR-CGO, a dataset for evaluating how well responses are tailored to provided CGOs. We benchmark several major LLMs on this task; finding GPT-4-Turbo performs significantly better than others. We also build automatic evaluation metrics, including an efficient and accurate BERT model that outperforms finetuned LLMs, investigate how to successfully tailor vaccine messaging to CGOs, and provide actionable recommendations from this investigation. Code and model weights: https://github.com/rickardstureborg/tailor-cgo Dataset: https://huggingface.co/datasets/DukeNLP/tailor-cgo

翻译：个性化聊天机器人交互的一种方式是与目标读者建立共同立场。在疫苗关切与错误信息领域，建立相互理解可能具有特别重要的影响。疫苗干预措施是一种旨在回应疫苗接种相关关切的信息传递形式。由于不同观点之间往往看似缺乏意识形态重叠，在该领域定制回应具有挑战性。本文定义了针对共同立场观点定制疫苗干预措施的任务。基于CGO定制回应需通过关联读者持有的观点或信念，实质性提升回答质量。本文介绍了TAILOR-CGO数据集，用于评估回应与给定CGO的契合程度。我们对多个主流大语言模型进行基准测试，发现GPT-4-Turbo的表现显著优于其他模型。同时构建了自动评估指标，包括一个高效精准的BERT模型——其性能优于微调后的大语言模型，探究了如何基于CGO成功定制疫苗信息，并为此项研究提供了可操作的实践建议。代码与模型权重：https://github.com/rickardstureborg/tailor-cgo 数据集：https://huggingface.co/datasets/DukeNLP/tailor-cgo