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Hallucination Detection and Hallucination Mitigation: An Investigation

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arxiv 2401.08358 v1 pith:K3WRYOA2 submitted 2024-01-16 cs.CL cs.AI

Junliang Luo , Tianyu Li , Di Wu , Michael Jenkin , Steve Liu , Gregory Dudek This is my paper
classification cs.CLcs.AI
keywords hallucinationllmscorrectdetectionmitigationproblemreportresponses
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Large language models (LLMs), including ChatGPT, Bard, and Llama, have achieved remarkable successes over the last two years in a range of different applications. In spite of these successes, there exist concerns that limit the wide application of LLMs. A key problem is the problem of hallucination. Hallucination refers to the fact that in addition to correct responses, LLMs can also generate seemingly correct but factually incorrect responses. This report aims to present a comprehensive review of the current literature on both hallucination detection and hallucination mitigation. We hope that this report can serve as a good reference for both engineers and researchers who are interested in LLMs and applying them to real world tasks.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models

    cs.CV 2026-07 conditional novelty 7.0 of 10

    Hallucinated captions systematically improve VLM accuracy on vision-language tasks across nine models and nine datasets, with gains linked to broadened semantic coverage and modulated reasoning entropy.

  2. Understanding the Supply Chain and Risks of Large Language Model Applications

    cs.SE 2025-07 conditional novelty 7.0 of 10

    A new benchmark dataset traces dependencies across 3,859 LLM applications, 109,211 models, 2,474 datasets, and 8,862 libraries, and finds widespread known vulnerabilities in application dependencies.

  3. Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning

    cs.CV 2025-06 reject novelty 5.0 of 10

    A two-stage, value-guided decoding strategy with a margin-based reward adjustment is claimed to yield more faithful, detailed VLM captions at about a quarter of VisVM's inference cost.

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