对比LLM随时间演化的差异，自动分析其观点与表达变化。
LLMTemporalComparator: A Tool for Analysing Differences in Temporal Adaptations of Large Language Models
- 基于关键词构建主题层级，系统化比较两版LLM输出
- 识别词汇、信息呈现和核心主题的时序差异
- 适合研究模型演化、社会趋势追踪的学者使用
本研究针对在不同时间数据上训练的大语言模型（LLMs）存在时间差异的问题，提出一种新型自动化分析工具。该系统通过用户定义的查询，系统性比较两个版本LLM的输出结果。首先，以用户指定关键词为基础生成层次化主题结构，实现主题类别的有序对比；随后，利用大模型对两版本输出进行评估，识别词汇使用、信息呈现方式及潜在主题上的差异。该全自动方法不仅加速了公众意见与文化规范演变的发现过程，也提升了对机器学习模型应对时间变化时适应性与鲁棒性的理解。本工作推动持续模型适配与对比摘要研究，助力构建能捕捉社会语境动态演化的更透明人工智能系统。
原文摘要 · Abstract (English)
This study addresses the challenges of analyzing temporal discrepancies in large language models (LLMs) trained on data from different time periods. To facilitate the automatic exploration of these differences, we propose a novel system that compares in a systematic way the outputs of two LLM versions based on user-defined queries. The system first generates a hierarchical topic structure rooted in a user-specified keyword, allowing for an organized comparison of topical categories. Subsequently, it evaluates the generated text by both LLMs to identify differences in vocabulary, information presentation, and underlying themes. This fully automated approach not only streamlines the identification of shifts in public opinion and cultural norms but also enhances our understanding of the adaptability and robustness of machine learning applications in response to temporal changes. By fostering research in continual model adaptation and comparative summarization, this work contributes to the development of more transparent machine learning models capable of capturing the nuances of evolving societal contexts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。