On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models? (2022.naacl-main)
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| Challenge: | Existing knowledge-grounded conversational benchmarks produce factually invalid statements, a phenomenon commonly called hallucination. |
| Approach: | They conduct a human study on knowledge-grounded conversational benchmarks and state-of-the-art models. |
| Outcome: | The findings raise important questions on the quality of existing datasets and models. |
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Che Jiang, Biqing Qi, Xiangyu Hong, Dayuan Fu, Yang Cheng, Fandong Meng, Mo Yu, Bowen Zhou, Jie Zhou
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| Challenge: | Prior studies have shown that fine-tuning on new knowledge can induce factual hallucinations in large language models (LLMs), leading to incorrect outputs when evaluated on previously known information. |
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The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models (2024.acl-long)
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| Challenge: | a growing number of researchers are studying the hallucination issue in large language models. |
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| Challenge: | Existing representations of hallucinations limit the types of errors that can be expressed, so we propose a new representation based on free-form textual descriptions, capturing the full range of possible errors. |
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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 dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations, but they are plagued by the Knowledge Hallucination problem. |
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| Challenge: | Large language models (LLMs) generate plausible-sounding responses that are factually incorrect. |
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| Challenge: | State-of-the-art language models (LMs) are notoriously susceptible to generating hallucinated information. |
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| Challenge: | State-of-the-art dialogue models suffer from factual incorrectness and hallucination of knowledge. |
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