Reducing Hallucinations in LLM-based Scientific Literature Analysis Using Peer Context Outlier Detection
cs.AI
Submitted: 2026-04-01
Updated: 2026-09-05
License: http://creativecommons.org/licenses/by/4.0/
The gist: Reducing hallucinations in Large Language Models (LLMs) is essential for accurate data extraction from large text corpora.
Terminology
Abstract
Reducing hallucinations in Large Language Models (LLMs) is essential for accurate data extraction from large text corpora. Current methods, like prompt engineering and chain-of-thought prompting, focus on individual documents and fail to consider relationships across a corpus. This paper introduces Peer Context Outlier Detection (P-COD), which uses inter-document relationships to improve extraction accuracy in scientific literature summarization, where papers with similar experiment settings should draw similar conclusions. By comparing extracted data to validated peer information within the corpus, we adjust confidence scores and flag low-confidence results for expert review. Our experiments demonstrate up to 98% precision in outlier detection across 6 scientific domains, reducing hallucinations and letting researchers focus on genuinely ambiguous cases.
Sources
- Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting
- DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models
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