迈向透明：通过可视化主题建模与语义框架探索大语言模型训练数据集
Towards Transparency: Exploring LLM Trainings Datasets through Visual Topic Modeling and Semantic Frame
AI总结：
本文提出Bunka软件，结合主题建模、二维制图与框架分析，以提高大语言模型训练数据集的透明度、质量并加速偏好数据微调。
摘要：
大语言模型（LLMs）如今代表人类做出许多决策：从回答问题到对事物进行分类，它们已成为日常生活的重要组成部分。尽管近年来计算能力和模型架构迅速扩展，但训练数据集的整理工作仍处于起步阶段。这种对训练数据集的重视不足导致大语言模型产生有偏见且低质量的内容。为了解决这一问题，我们提出了Bunka，一款利用人工智能与认知科学来改进文本数据集精炼的软件。我们展示了主题建模与二维制图相结合如何提高数据集的透明度。随后，我们展示了相同的主题建模技术如何应用于偏好数据集，以加速微调过程并提升模型在不同基准上的能力。最后，我们展示了使用框架分析如何洞察训练语料库中现有的偏见。总体而言，我们认为需要更好的工具来探索并提高大语言模型训练数据集的质量与透明度。
英文摘要：
LLMs are now responsible for making many decisions on behalf of humans: from answering questions to classifying things, they have become an important part of everyday life. While computation and model architecture have been rapidly expanding in recent years, the efforts towards curating training datasets are still in their beginnings. This underappreciation of training datasets has led LLMs to create biased and low-quality content. In order to solve that issue, we present Bunka, a software that leverages AI and Cognitive Science to improve the refinement of textual datasets. We show how Topic Modeling coupled with 2-dimensional Cartography can increase the transparency of datasets. We then show how the same Topic Modeling techniques can be applied to Preferences datasets to accelerate the fine-tuning process and increase the capacities of the model on different benchmarks. Lastly, we show how using Frame Analysis can give insights into existing biases in the training corpus. Overall, we argue that we need better tools to explore and increase the quality and transparency of LLMs training datasets.
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。
