Clinical Reading Comprehension with Encoder-Decoder Models Enhanced by Direct Preference Optimization
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917727739838464 |
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| author | Nahian, Md Sultan Al Kavuluru, Ramakanth |
| author_facet | Nahian, Md Sultan Al Kavuluru, Ramakanth |
| contents | Extractive question answering over clinical text is a crucial need to help deal with the deluge of clinical text generated in hospitals. While encoder models (e.g., BERT) have been popular for this reading comprehension task, recently encoder-decoder models (e.g., T5) are on the rise. There is also the emergence of preference optimization techniques to align decoder-only LLMs with human preferences. In this paper, we combine encoder-decoder models with the direct preference optimization (DPO) method to improve over prior state of the art for the RadQA radiology question answering task by 12-15 F1 points. To the best of our knowledge, this effort is the first to show that DPO method also works for reading comprehension via novel heuristics to generate preference data without human inputs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_14000 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Clinical Reading Comprehension with Encoder-Decoder Models Enhanced by Direct Preference Optimization Nahian, Md Sultan Al Kavuluru, Ramakanth Information Retrieval Computation and Language Machine Learning Extractive question answering over clinical text is a crucial need to help deal with the deluge of clinical text generated in hospitals. While encoder models (e.g., BERT) have been popular for this reading comprehension task, recently encoder-decoder models (e.g., T5) are on the rise. There is also the emergence of preference optimization techniques to align decoder-only LLMs with human preferences. In this paper, we combine encoder-decoder models with the direct preference optimization (DPO) method to improve over prior state of the art for the RadQA radiology question answering task by 12-15 F1 points. To the best of our knowledge, this effort is the first to show that DPO method also works for reading comprehension via novel heuristics to generate preference data without human inputs. |
| title | Clinical Reading Comprehension with Encoder-Decoder Models Enhanced by Direct Preference Optimization |
| topic | Information Retrieval Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2407.14000 |