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Cognitive Mirage: A Review of Hallucinations in Large Language Models

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arxiv 2309.06794 v1 pith:5ZXMX3WH submitted 2023-09-13 cs.CL cs.AIcs.LG

classification cs.CLcs.AIcs.LG
keywords hallucinationsgenerationresearchtextdetectiondirectionsfutureimprovement
verification ladder T0 review T1 audit T2 compute T3 formal
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As large language models continue to develop in the field of AI, text generation systems are susceptible to a worrisome phenomenon known as hallucination. In this study, we summarize recent compelling insights into hallucinations in LLMs. We present a novel taxonomy of hallucinations from various text generation tasks, thus provide theoretical insights, detection methods and improvement approaches. Based on this, future research directions are proposed. Our contribution are threefold: (1) We provide a detailed and complete taxonomy for hallucinations appearing in text generation tasks; (2) We provide theoretical analyses of hallucinations in LLMs and provide existing detection and improvement methods; (3) We propose several research directions that can be developed in the future. As hallucinations garner significant attention from the community, we will maintain updates on relevant research progress.

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Cited by 6 Pith papers

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

  1. AISPA: User-Centric System Prompt Auditing for Large Language Model Applications

    cs.AI 2026-07 conditional novelty 6.5 of 10

    A human-in-the-loop audit of system prompts from 88 commercial AI products finds protective instructions nearly universal yet incomplete, with ~40% of products containing at least one user-harmful directive.

  2. Beyond the Syntax: Do Security Experts Trust LLMs for NIDS Rule Engineering?

    cs.CR 2026-07 conditional novelty 6.0 of 10

    A user study with 10 security experts reveals that while large LLMs (≥70B) generate syntactically valid NIDS rules, experts deem only 37.5% deployable due to low specificity and logic hallucinations, viewing LLMs as s...

  3. Beyond Correctness: Rewarding Faithful Reasoning in Retrieval-Augmented Generation

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    A turn-level faithfulness reward improves a Search-R1-style agent's Information-Think and Think-Answer faithfulness as judged by the same reward model used for training, while task accuracy is roughly unchanged.

  4. Hallucination Detection and Mitigation with Diffusion in Multi-Variate Time-Series Foundation Models

    cs.LG 2025-07 conditional novelty 6.0 of 10

    Pre-trained multivariate time-series imputation models frequently return values that violate known relations between variables, and a diffusion-based score can detect and filter these errors.

  5. Unified Multimodal Understanding via Byte-Pair Visual Encoding

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    Priority-guided byte-pair encoding of quantized image patches plus curriculum training yields an 8B discrete-token MLLM competitive with continuous-embedding models on VQA and multimodal benchmarks.

  6. Provably Secure Retrieval-Augmented Generation

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    SAG encrypts RAG knowledge bases and claims formal security, but its proofs are flawed and its benchmarks guarantee zero attack success by design.

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