学习个性化对齐以评估开放式文本生成
Learning Personalized Alignment for Evaluating Open-ended Text Generation
AI总结：
针对传统开放式文本生成评估指标与人类判断相关性弱、难适配多样偏好的问题，提出可解释的个性化评估框架PerSE，基于13B LLaMA-2实现，在相关性、准确率及领域迁移性上均优于GPT-4。
摘要：
近期研究日益关注评估大语言模型（LLMs）与多样人类价值观及偏好的对齐情况，尤其是针对故事生成这类开放式任务。传统评估指标严重依赖与人工撰写参考文本的词汇相似度，往往与人类判断的相关性较差，且无法考量与人类偏好多样性的对齐。为应对这些挑战，我们提出PerSE——一种可解释的评估框架，旨在评估与特定人类偏好的对齐程度。该框架经调优后可从上下文内的个人档案中推断特定偏好，并评估生成内容与个人偏好的对齐情况。PerSE通过提供详细评语和细粒度评分提升可解释性，助力更具个性化的内容生成。我们基于13B LLaMA-2的PerSE在零样本评审者设置下，相比GPT-4的Kendall相关系数提升15.8%，准确率提升13.7%；在新领域的Kendall相关系数上也比GPT-4高出46.01%，展现出其可迁移性。
英文摘要：
Recent research has increasingly focused on evaluating large language models' (LLMs) alignment with diverse human values and preferences, particularly for open-ended tasks like story generation. Traditional evaluation metrics rely heavily on lexical similarity with human-written references, often showing poor correlation with human judgments and failing to account for alignment with the diversity of human preferences. To address these challenges, we introduce PerSE, an interpretable evaluation framework designed to assess alignment with specific human preferences. It is tuned to infer specific preferences from an in-context personal profile and evaluate the alignment between the generated content and personal preferences. PerSE enhances interpretability by providing detailed comments and fine-grained scoring, facilitating more personalized content generation. Our 13B LLaMA-2-based PerSE shows a 15.8% increase in Kendall correlation and a 13.7% rise in accuracy with zero-shot reviewers compared to GPT-4. It also outperforms GPT-4 by 46.01% in Kendall correlation on new domains, indicating its transferability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
