适配大语言模型用于内容审核：数据工程与监督微调中的陷阱
Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning
AI总结：
本文针对内容审核任务，对比判别式与生成式模型，提出在LLM微调中加入推理过程可有效缓解过拟合，给出私有化部署LLM微调的完整流程，为领域研究者提供经验参考。
摘要：
如今，每天有数十亿人在互联网上交流并表达观点。遗憾的是，并非所有这些表达都是友好或合规的，这使得内容审核成为一项不可或缺的任务。常见方法是使用判别式模型对内容进行分类，但这种方法通常需要严格的数据工程，否则会面临不可接受的过拟合问题。随着近年来大语言模型（Large Language Models, LLMs）的成功发展，基于LLM的方法已成为处理各领域任务的可行解决方案。得益于基础模型的知识，我们可以通过微调这些基础模型，用有限的数据开发出更鲁棒的可私有化部署模型。此外，作为生成式模型，它能够对审核过程提供详细分析，提升可解释性。本文介绍了如何微调可私有化部署的LLM模型以用于内容审核。具体而言，我们以内容审核为例，讨论了判别式模型与生成式模型之间的差异。此外，我们揭示了在LLM微调过程中加入推理过程可以有效缓解过拟合，即使部署阶段不允许模型直接输出推理过程也是如此。我们提出了一套用于垂直领域部署中LLM微调的完整流程，涵盖从数据收集与构建到模型训练与过拟合消除的全环节。我们在本文中报告了整个研究过程与关键发现，希望能为在特定领域研究中微调私有化部署模型的研究者提供宝贵经验。
英文摘要：
Nowadays, billions of people engage in communication and express their opinions on the internet daily. Unfortunately, not all of these expressions are friendly or compliant, making content moderation an indispensable task. A common approach is to use a discriminative model to classify the content, but this method often requires strict data engineering, otherwise it will face unacceptable overfitting. With the successful development of Large Language Models (LLMs) in recent years, LLM-based methods have become a feasible solution for handling tasks in various domains. Thanks to the knowledge of the foundation models, we can develop more robust privately deployed models with limited data via fine-tuning these foundation models. Moreover, as a generative model, it can provide detailed analysis of the review process, enhancing interpretability. In this paper, we introduce how to fine-tune a LLM model that can be privately deployed for content moderation. Specifically, we discuss the differences between discriminative and generative models using content moderation as an example. Additionally, we reveal that incorporating reasoning processes during the fine-tuning of LLMs can effectively alleviate overfitting, even if the model is not allowed to directly output reasoning processes during deployment. We present a complete process, from data collection and construction to model training and overfitting elimination, for fine-tuning LLMs in vertical domain deployments. We report the entire research process and the key findings in this paper, hoping to provide valuable experience for researchers who are fine-tuning privately deployed models in their domain-specific research.
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。
