ChildEval：当大语言模型遇到儿童个性
ChildEval: When large language models meet children's personalities
AI总结：
提出ChildEval基准，通过合成3-6岁儿童个性档案和偏好（显式或隐式表达），评估大语言模型在长对话中推断并遵循儿童偏好的能力，实验表明微调可提升儿童中心性能。
摘要：
虽然大语言模型（LLM）使得个性化聊天机器人成为可能，但它们在儿童中心个性化方面的有效性仍不明确，因为缺乏对儿童特定偏好的系统评估。为填补这一空白，我们引入了ChildEval，一个用于评估LLM在长上下文对话中推断和遵循儿童中心偏好能力的基准。ChildEval包含29K个3-6岁儿童的合成个性档案，提供相对静态的背景信息。每个个性档案关联一个儿童偏好——可能与个性一致、冲突或独立——通过单句显式表达或6-10轮对话隐式表达。显式和隐式偏好旨在反映相同的潜在偏好，但表达方式不同，捕捉偏好表达的动态方面而非静态个性的变化。该基准涵盖五个顶层类别和十四个子类别，覆盖儿童的日常生活和发展。我们进一步提出了细粒度、以儿童为中心的评估协议，以系统评估开源LLM。实验结果表明，不同的个性化表示如何影响LLM的响应，并表明在ChildEval上进行微调可以提升儿童中心性能。我们的代码和数据集可在https://github.com/ziyanluo/ChildEval获取。
英文摘要：
While LLMs enable personalized chatbots, their effectiveness in child-centered personalization remains unclear, as systematic evaluation of child-specific preferences is still lacking. To address this gap, we introduce ChildEval, a benchmark for evaluating LLMs' ability to infer and follow child-centered preferences in long-context conversations. ChildEval contains 29K synthesized persona profiles of children aged 3-6, providing relatively static background information. Each persona is associated with a child preference-which may align with, conflict with, or be independent of the persona-expressed either explicitly in a single sentence or implicitly through 6-10 turn dialogues. Explicit and implicit preferences are designed to reflect the same underlying preference but differ in expression, capturing dynamic aspects of preference expression rather than changes in the static persona. The benchmark spans five top-level and fourteen sub-level categories covering children's daily lives and development. We further propose fine-grained, child-centric evaluation protocols to systematically assess open-source LLMs. Experimental results demonstrate how different personalized representations affect LLM responses and suggest that finetuning on ChildEval can enhance child-centered performance. Our code and dataset are available at https://github.com/ziyanluo/ChildEval.
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。
