Papers by Jaepill Choi
Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information (2024.findings-naacl)
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| Challenge: | Prior studies have attempted to enhance faithfulness of abstractive summarization, yet hallucination remains a persistent challenge. |
| Approach: | They propose a decoding strategy that adjusts the generation probability of each token by comparing it with the token’s marginal probability within the domain of the source text. |
| Outcome: | The proposed method significantly improves faithfulness and source relevance on the XSUM dataset. |
Model-based Preference Optimization in Abstractive Summarization without Human Feedback (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) can generate fluent summaries but often introduce inaccuracies by hallucinating content not found in the source document. |
| Approach: | They propose a method to fine-tune Large Language Models for improved summarization abilities without any human feedback. |
| Outcome: | The proposed method significantly improves the quality of generated summaries without any human feedback. |