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Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations

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arxiv 2310.03951 v2 pith:D4XXVKUC submitted 2023-10-06 cs.CL cs.AI

Deren Lei , Yaxi Li , Mengya Hu , Mingyu Wang , Vincent Yun , Emily Ching , Eslam Kamal This is my paper · ORCID
classification cs.CLcs.AI
keywords hallucinationlanguagellmsdetectionframeworknaturalchaingenerate
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) can generate fluent natural language texts when given relevant documents as background context. This ability has attracted considerable interest in developing industry applications of LLMs. However, LLMs are prone to generate hallucinations that are not supported by the provided sources. In this paper, we propose a hierarchical framework to detect and mitigate such ungrounded hallucination. Our framework uses Chain of Natural Language Inference (CoNLI) for hallucination detection and hallucination reduction via post-editing. Our approach achieves state-of-the-art performance on hallucination detection and enhances text quality through rewrite, using LLMs without any fine-tuning or domain-specific prompt engineering. We show that this simple plug-and-play framework can serve as an effective choice for hallucination detection and reduction, achieving competitive performance across various contexts.

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Cited by 5 Pith papers

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

  1. Decomposed Entailment for Factuality Checking and Hallucination Detection

    cs.CL 2026-08 conditional novelty 6.0 of 10

    HallDetect detects source-grounded hallucinations by decomposing responses into atomic claims and verifying each with a compact NLI model over multi-scale source chunks, outperforming frugal generative baselines on th...

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    cs.CL 2025-07 conditional novelty 5.0 of 10

    CCL-XCoT combines curriculum-based contrastive pretraining with cross-lingual chain-of-thought fine-tuning, lifting hallucination-free rates in low-resource QA from 1-18% to 55-74%.

  3. TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs

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    TruthTorchLM is a new open-source library that standardizes 30+ LLM truthfulness prediction methods and benchmarks them on three datasets.

  4. Inteligencia Artificial jur\'idica y el desaf\'io de la veracidad: an\'alisis de alucinaciones, optimizaci\'on de RAG y principios para una integraci\'on responsable

    cs.AI 2025-09 conditional novelty 4.0 of 10

    Legal AI hallucination persists in commercial RAG tools (17-34%+ of queries), so the report argues the fix is consultative, source-citing system design plus mandatory human oversight, not better generative models.

  5. Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities

    eess.SP 2025-09 conditional novelty 4.0 of 10

    AI can be used to generate interactive signal processing courseware, but the paper offers no evidence that students learn better from it.

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