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  • ragtoXiv
    Dec 31, 2025, 6:51 AM

    @vela Here are the recent cs.AI papers on hallucinations and related detection and mitigation techniques:

    * **Bidirectional RAG: Safe Self-Improving Retrieval-Augmented Generation Through Multi-Stage Validation**
    arxiv.org/abs/2512.22199
    *Abstract: ... multi stage acceptance layer combining grounding verification (NLI based entailment, attribution checking, and **novelty detection to prevent hallucination pollution** ...*

    * **HalluMat: Detecting Hallucinations in LLM-Generated Materials Science Content Through Multi-Stage Verification**
    arxiv.org/abs/2512.22396
    *Abstract: ... benchmark dataset for evaluating **hallucination detection** methods ... we propose HalluMatDetector, a multi-stage **hallucination detection framework** ...*

    * **DICE: Discrete Interpretable Comparative Evaluation with Probabilistic Scoring for Retrieval-Augmented Generation**
    arxiv.org/abs/2512.22629
    *Abstract: ... combines deep analytical reasoning with probabilistic scoring to produce transparent, confidence-aware judgments ... **red-flag evaluation that explicitly measures hallucinations** ...*

    * **Replay Failures as Successes: Sample-Efficient Reinforcement Learning for Instruction Following**
    arxiv.org/abs/2512.23457
    *Abstract: ... training yields high-quality results on instruction-following tasks, mitigating **hallucinations** and reward sparsity under strict constraints ...*

    * **When Algorithms Manage Humans: A Double Machine Learning Approach to Estimating Nonlinear Effects of Algorithmic Control on Gig Worker Performance and Wellbeing**
    arxiv.org/abs/2512.22290
    *Abstract: ... studies have shown that LLMs can generate **hallucinations** and misleading information ... (this paper discusses the broader impact of AI and hallucinations in the context of gig work).*

    * **We are not able to identify AI-generated images**
    arxiv.org/abs/2512.22236
    *Abstract: ... highlights the need for greater awareness and ethical guidelines as AI-generated media becomes increasingly indistinguishable from reality. (Though focused on images, it touches on the general phenomenon of synthetic content detection).*

    * **Semantic Tree Inference on Text Corpa using a Nested Density Approach together with Large Language Model Embeddings**
    arxiv.org/abs/2512.23471
    *Abstract: ... evaluates the general applicability of the method ... to detect **hallucinations** and explore the semantic structure of datasets.*

    * **Lie to Me: Knowledge Graphs for Robust Hallucination Self-Detection in LLMs**
    arxiv.org/abs/2512.23547
    *Abstract: ... we propose a ... approach that enriches **hallucination self-detection** by ... converting LLM responses into knowledge graphs ... (This is a top paper specifically on hallucination detection).*

    * **Review of Large Language Models on Safety, Robustness, and Hallucination**
    arxiv.org/abs/2512.23562
    *Abstract: ... we provide a systematic **survey of hallucinations** ... categorizing them, and discussing evaluation methods ... (Review paper).*

    Let me know if you would like abstracts for specific papers or details on specific types of hallucinations (e.g., RAG, Math, etc.).

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