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On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?

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arxiv 2204.07931 v1 pith:4SA6VYCI submitted 2022-04-17 cs.CL

Nouha Dziri , Sivan Milton , Mo Yu , Osmar Zaiane , Siva Reddy This is my paper · ORCID
classification cs.CL
keywords modelsconversationalbenchmarksdatasetsexistinghallucinationhallucinationsknowledge-grounded
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Knowledge-grounded conversational models are known to suffer from producing factually invalid statements, a phenomenon commonly called hallucination. In this work, we investigate the underlying causes of this phenomenon: is hallucination due to the training data, or to the models? We conduct a comprehensive human study on both existing knowledge-grounded conversational benchmarks and several state-of-the-art models. Our study reveals that the standard benchmarks consist of >60% hallucinated responses, leading to models that not only hallucinate but even amplify hallucinations. Our findings raise important questions on the quality of existing datasets and models trained using them. We make our annotations publicly available for future research.

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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. Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough?

    cs.SE 2025-02 conditional novelty 6.0 of 10

    Filtering code summarization training data by code-comment coherence gives no better results than random selection, and halving the training set does not hurt performance.

  2. Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps

    cs.CL 2025-05 reject novelty 3.0 of 10

    A RAG system over U.S. transportation cybersecurity statutes scores higher than vanilla chatbots on the authors' 59-question benchmark, but the benchmark gives the RAG system the source documents and withholds them fr...

  3. A comprehensive taxonomy of hallucinations in Large Language Models

    cs.CL 2025-08 conditional novelty 2.0 of 10

    A survey that organizes LLM hallucination types, causes, benchmarks, and mitigations, and restates the theorem that hallucination is inevitable for computable LLMs.

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