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Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data

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arxiv 2010.05873 v1 pith:2FGB4TT5 submitted 2020-10-12 cs.CL

Katja Filippova This is my paper · ORCID
classification cs.CL
keywords datatextcorpushallucinationsinputnoisenoisytechnique
verification ladder T0 review T1 audit T2 compute T3 formal

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Neural text generation (data- or text-to-text) demonstrates remarkable performance when training data is abundant which for many applications is not the case. To collect a large corpus of parallel data, heuristic rules are often used but they inevitably let noise into the data, such as phrases in the output which cannot be explained by the input. Consequently, models pick up on the noise and may hallucinate--generate fluent but unsupported text. Our contribution is a simple but powerful technique to treat such hallucinations as a controllable aspect of the generated text, without dismissing any input and without modifying the model architecture. On the WikiBio corpus (Lebret et al., 2016), a particularly noisy dataset, we demonstrate the efficacy of the technique both in an automatic and in a human evaluation.

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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. Interpretable Zero-shot Learning with Infinite Class Concepts

    cs.CV 2025-05 conditional novelty 6.0 of 10

    InfZSL generates unlimited LLM-based visual concepts for each class, selects transferable and discriminative ones via a concept-entropy ranking, and builds interpretable class embeddings that improve zero-shot recogni...

  2. Hallucination Detection with Small Language Models

    cs.CL 2025-06 reject novelty 5.0 of 10

    A multi-small-model ensemble with sentence splitting, z-score normalization, and harmonic mean detects hallucinations in RAG answers with a reported 10% F1 gain over single-model baselines.

  3. EmotionHallucer: Evaluating Emotion Hallucinations in Multimodal Large Language Models

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    Multimodal LLMs frequently accept hallucinated emotion claims on a new adversarial benchmark, with the worst failures on image, audio, and video perception rather than on textbook emotion knowledge.

  4. OpenFActScore: Open-Source Atomic Evaluation of Factuality in Text Generation

    cs.CL 2025-07 conditional novelty 4.0 of 10

    Using Olmo to extract atomic facts and Gemma to verify them against Wikipedia, OpenFActScore reproduces the original FActScore ranking of 10 LLMs with a Pearson correlation above 0.99.

  5. Bridging the Safety Gap: A Guardrail Pipeline for Trustworthy LLM Inferences

    cs.AI 2025-02 conditional novelty 3.0 of 10

    A guardrail pipeline combining detection, retrieval grounding, rule-based wrappers, and a repair model is reported to match OpenAI moderation and fix 80.7 percent of hallucinated HaluEval answers.

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