arXiv:2606.28062v1 Announce Type: cross Abstract: 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 p
The proliferation of complex and often conflicting data, combined with the rapid advancements in Large Language Models (LLMs), has created an opportune moment for applying LLMs to sophisticated data integration problems like truth discovery.
This research addresses a fundamental challenge in data quality and reliability, promising to significantly enhance the accuracy and utility of large datasets, which is critical for decision-making across industries.
LLMs are moving beyond generative text into complex data integration and validation, potentially automating and improving data fusion processes that were previously manual or highly specialized.
- · Data-intensive industries
- · Analytics and AI software providers
- · Data scientists
- · Financial services
- · Manual data reconciliation services
- · Legacy data integration vendors that don't adapt
Improved data quality and trust within organizations using LLM-powered data fusion.
Faster and more reliable insights derived from fused data, leading to better strategic decisions and operational efficiencies.
The development of truly autonomous data management systems capable of self-healing and continuous validation, further collapsing data engineer workflows.
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Read at arXiv cs.AI