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. |
| Outcome: | Experiments on the E2E challenge dataset show that the proposed framework reduces relative unaligned noise by 50% compared with the current state-of-the-art ensemble generator. |
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| Challenge: | Current-day large language models generate coherent, grammatical, and seemingly meaningful text, but are prone to hallucinating incorrect information. |
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| Challenge: | generative large language models produce hallucinations that are not aligned with world knowledge or input context. |
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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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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. |
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HALLUCANA: Fixing LLM Hallucination with A Canary Lookahead (2025.findings-naacl)
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| Challenge: | Existing methods to detect factual hallucinations w.r.t. world knowledge require assistive generations (samples, drafts, etc.) Existing approaches to factuality hallucinism detection, such as SelfCheckGPT, require assistively generation (sequences, Drafts, and etc.). Existing studies on factualism hallucinosation detection require assistives generations (Sample, draft, etc). |
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