用大模型解决表格数据中多源信息冲突，区分单值与多值场景。
Single and Multi Truth Data Fusion using Large Language Models
- 设计多种提示策略，适配单值和多值数据融合任务。
- 在三个数据集上超越传统方法，验证大模型有效性。
- 提供开源代码，适合数据清洗与集成研究者使用。
数据融合（又称真相发现）是数据集成中的核心问题，旨在当多个来源提供可能冲突的属性值时，确定每个对象属性的正确值。该任务分为两类：单真相场景（每属性仅一个正确值）和多真相场景（允许多个有效值并存）。本文研究大语言模型在表格数据融合任务中的应用，通过实证评估了涵盖单真相与多真相场景的多种提示策略。在三个基准数据集上，对领域相关、领域无关、零样本及单样本提示进行了测试。实验结果表明，基于大模型的方法在所有数据集上均优于传统无监督真相发现方法（如DART和LTM）。本研究代码已公开于GitHub。
原文摘要 · Abstract (English)
Data fusion, also known as truth discovery, is a data integration problem that aims to determine the correct value or set of values for each attribute of an object when presented with potentially conflicting values from multiple sources. Data fusion tasks belong to two main categories: single-truth scenarios, where each attribute has only one correct value, and multi-truth scenarios, where multiple values can be valid simultaneously. This paper investigates the use of Large Language Models (LLMs) in data fusion tasks for tabular data. Various prompting strategies, encompassing both single-truth and multi-truth scenarios, are investigated empirically. Domain-dependent, domain-independent, zero-shot and one-shot prompts are evaluated on three different benchmark datasets. Experimental results demonstrate that LLM-based approaches outperform traditional unsupervised truth discovery methods, such as DART and LTM, across all datasets. The codebase of this study has been made publicly available on GitHub.
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