超越微调：缓解大型语言模型在数据分析中幻觉的有效策略
Beyond Fine-Tuning: Effective Strategies for Mitigating Hallucinations in Large Language Models for Data Analytics
AI总结：
针对 LLM 在数据分析自然语言查询中的幻觉问题，本文评估结构化输出、规则执行、提示增强与语义层集成四种策略，证明其优于传统微调并提升结果可靠性。
摘要：
大型语言模型（LLM）在自然语言处理中已变得日益重要，能够通过自然语言查询实现先进的数据分析。然而，这些模型经常产生“幻觉”——即不准确或捏造的信息——这会削弱其在关键数据驱动决策中的可靠性。应对幻觉挑战对于提高 LLM 在处理自然语言查询时的准确性和可信赖性至关重要。本研究聚焦于缓解 LLM 中的幻觉，特别是在数据分析场景下。我们引入并评估了四种有针对性的策略：结构化输出生成、严格规则执行、系统提示增强和语义层集成。研究结果表明，这些方法在减少幻觉方面比传统微调方法更有效，为在数据分析的自然语言查询中部署 LLM 提供了更可靠的框架。本研究证明了这些策略在提高 LLM 驱动数据查询准确性方面的潜力，可确保在数据驱动环境中获得更可靠的结果。
英文摘要：
Large Language Models (LLMs) have become increasingly important in natural language processing, enabling advanced data analytics through natural language queries. However, these models often generate "hallucinations"-inaccurate or fabricated information-that can undermine their reliability in critical data-driven decision-making. Addressing the challenge of hallucinations is essential to improve the accuracy and trustworthiness of LLMs in processing natural language queries. This research focuses on mitigating hallucinations in LLMs, specifically within the context of data analytics. We introduce and evaluate four targeted strategies: Structured Output Generation, Strict Rules Enforcement, System Prompt Enhancements, and Semantic Layer Integration. Our findings show that these methods are more effective than traditional fine-tuning approaches in reducing hallucinations, offering a more reliable framework for deploying LLMs in natural language queries for data analytics. This research demonstrates the potential of these strategies to enhance the accuracy of LLM-driven data queries, ensuring more dependable results in data-driven environments.
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。
