Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text Generation (2023.emnlp-main)
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| Challenge: | Hallucination of text lacking grounding in input data is a problem in neural data-to-text generation. |
| Approach: | They propose to combine probabilistic output of a generator language model with the output of an “text critic” classifier which guides the generation by assessing the match between the input data and the generated text. |
| Outcome: | The proposed method improves on the WebNLG and OpenDialKG benchmarks. |
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| Challenge: | Hallucinations occur when the target side sentence is detached from the source side sentence, or in other words, when there is a low contribution of the source sentence to the generation of the target sentence. |
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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: | Recent work has demonstrated reinforcement learning and weighted decoding as effective approaches to achieve a higher level of language control and quality with pros and cons. |
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| Challenge: | Machine Translation (MT) systems based on fine-tuned large language models (LLMs) are at a higher risk of generating hallucinations, which can severely undermine user’s trust and safety. |
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| Challenge: | Existing methods for hallucination detection are expensive and outdated . despite the popularity of LLMs, the issue of hallucinosity poses significant concerns for downstream users. |
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