Elastic Weight Removal for Faithful and Abstractive Dialogue Generation (2024.naacl-long)
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| Challenge: | Current-day large language models generate coherent, grammatical, and seemingly meaningful text, but are prone to hallucinating incorrect information. |
| Approach: | They propose to ‘subtract’ parameters of a model trained to hallucinate from a dialogue response generation model to ‘negate’ the contribution of such hallucinatedexamples from it. |
| Outcome: | The proposed method reduces hallucinations and discourages extractive responses, which are often a consequence of reducing hallucines by encouraging copy-pasting of document spans. |
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| Challenge: | Existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations, but they are plagued by the Knowledge Hallucination problem. |
| Approach: | They propose a method that exploits the dialogue-knowledge interaction to reduce hallucination by using external knowledge resources to generate more informative responses. |
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Towards Fewer Hallucinations in Knowledge-Grounded Dialogue Generation via Augmentative and Contrastive Knowledge-Dialogue (2023.acl-short)
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| Challenge: | Existing knowledge-grounded dialogue generation models face the hallucination problem . Existing models generate inappropriate knowledge and generate inconsistent responses . |
| Approach: | They propose an Augmentative and Contrastive Knowledge Dialogue Expansion Framework to enhance existing knowledge dialogue models by polarizing optimization objectives and weak knowledge generation ability. |
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Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding (2021.emnlp-main)
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| Challenge: | Dialogue systems that generate factually incorrect responses are often unfitful and hallucinate factuality invalid. |
| Approach: | They propose a method to improve faithfulness and reduce hallucination of neural dialogue systems to known facts supplied by a Knowledge Graph. |
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A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation (P19-1)
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| Challenge: | Recent neural language generation systems often hallucinate contents when trained on loosely corresponding pairs of the input structure and text. |
| Approach: | They propose to integrate a language understanding module for data refinement with self-training iterations to induce strong equivalence between the input data and the paired text. |
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Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection (2023.tacl-1)
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| Challenge: | Neural sequence generation models produce outputs that are unrelated to the source text, and are potentially harmful, yet it remains unclear in what conditions they arise and how to mitigate their impact. |
| Approach: | They first identify internal model symptoms of hallucinations by analyzing the relative token contributions to the generation in contrastive hallucinous vs. non-hallucinated outputs generated via source perturbations. |
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RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding (2023.findings-acl)
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| Challenge: | Existing knowledge-grounded dialogue systems generate accurate and informative responses, but they are prone to hallucination problems. |
| Approach: | They propose a method to generate hallucinated responses using knowledge graphs . they propose local knowledge grounding to combine textual embeddings with corresponding KG embeddments . a global knowledge ground technique is also proposed to equip RHO with multi-hop reasoning abilities . |
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Alleviating Hallucinations of Large Language Models through Induced Hallucinations (2025.findings-naacl)
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| Challenge: | Existing studies have shown that large language models generate inaccurate or fabricated information, a phenomenon known as hallucinations. |
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Detecting Hallucinated Content in Conditional Neural Sequence Generation (2021.findings-acl)
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Chunting Zhou, Graham Neubig, Jiatao Gu, Mona Diab, Francisco Guzmán, Luke Zettlemoyer, Marjan Ghazvininejad
| Challenge: | Neural sequence models can generate fluent sentences, but they can also hallucinate additional content not supported by the input. |
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Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better (2023.acl-long)
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| Challenge: | a recent study shows that without artificially encouraging models to hallucinate, existing methods fall short . hallucinations are cases when the model generates output that is partially or fully unrelated to the source sentence. |
| Approach: | They propose a method that evaluates the percentage of the source contribution to a generated translation. |
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Improving Faithfulness by Augmenting Negative Summaries from Fake Documents (2022.emnlp-main)
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| Challenge: | Current abstractive summarization systems tend to hallucinate unfaithful content . however, the most common method does not disentangle factual errors from other errors. |
| Approach: | They propose a back-translation-style approach to augment negative samples that mimic factual errors made by the model. |
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