arXiv:2605.28828v1 Announce Type: cross Abstract: Large Language Models (LLMs) achieve impressive performance across many tasks but remain prone to hallucination, especially in long-form generation where redundant retrieved contexts and lengthy reasoning chains amplify factual errors. Recent studies highlight a critical phenomenon: the closer key information appears to the model outputs, the higher the factual accuracy. However, existing retrieval-augmented language models (RALMs) lack effective mechanisms to ensure this proximity - external evidence is injected into reasoning via multi-turn r
This research addresses a critical limitation of Large Language Models (LLMs) – hallucination – which is increasingly prominent as LLMs are deployed in sensitive applications requiring factual accuracy.
Improving factual accuracy in LLMs, especially for long-form generation, is crucial for their broader adoption in enterprise and mission-critical applications, reducing risks associated with misinformation.
This paper introduces a novel approach, Micro-Macro Retrieval, that directly tackles long-form hallucination by ensuring retrieved context is better integrated and positioned within model outputs.
- · LLM developers
- · Enterprises adopting AI
- · Users of AI-generated content
- · Companies relying on less accurate RALMs
- · Processes vulnerable to AI hallucination
Reduced factual errors in long-form AI-generated text, leading to more reliable AI applications.
Increased trust in AI systems for tasks requiring synthesis and summarization of complex information.
Accelerated integration of advanced AI into industries like legal, medical, and scientific research where accuracy is paramount.
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Read at arXiv cs.AI