mlsanityData to 7 Oct 2026

Reducing Hallucinations in LLM-based Scientific Literature Analysis Using Peer Context Outlier Detection

arXiv 2604.01461 ↗First posted 1 Apr 2026cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 1 Apr 2026.

Builds on

16 of 24 references.

References · 16

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
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  6. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 612
  7. 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 477
  8. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 428
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  10. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answeringcited by 217
  11. 2023DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Modelscited by 124
  12. 2024Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflectioncited by 40
4 more
  1. 2022AdaPrompt: Adaptive Model Training for Prompt-based NLPcited by 30
  2. 2024A General Framework for Producing Interpretable Semantic Text Embeddingscited by 10
  3. 2025Agentic Services Computingcited by 6
  4. 2024DeepDiveAI: Identifying AI Related Documents in Large Scale Literature Datacited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Hallucination as an Anomaly: Dynamic Intervention via Probabilistic Circuitscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models
  2. 2023DelucionQA: Detecting Hallucinations in Domain-specific Question Answering
  3. 2026MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoning
  4. 2024Abstention vs. Hallucination: Benchmarking LLM Source Attribution for Scientific Citations
  5. 2024FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs
  6. 2024Improving Scientific Hypothesis Generation with Knowledge Grounded Large Language Models
  7. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
  8. 2024The HalluRAG Dataset: Detecting Closed-Domain Hallucinations in RAG Applications Using an LLM's Internal States