TOP-Training: Target-Oriented Pretraining for Medical Extractive Question Answering

• URL: http://arxiv.org/abs/2310.16995v2
• Date: Thu, 12 Dec 2024 13:33:56 GMT
• Title: TOP-Training: Target-Oriented Pretraining for Medical Extractive Question Answering
• Authors: Saptarshi Sengupta, Connor Heaton, Shreya Ghosh, Wenpeng Yin, Preslav Nakov, Suhang Wang,
• Abstract summary: We study extractive question-answering in the medical domain (Medical-EQA)<n>This problem has two main challenges: (i) domain specificity, and (ii) extraction-based answering style.<n>We propose TOP-Training, a target-oriented pre-training paradigm.
• Score: 53.92585020805746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study extractive question-answering in the medical domain (Medical-EQA). This problem has two main challenges: (i) domain specificity, as most AI models lack necessary domain knowledge, and (ii) extraction-based answering style, which restricts most autoregressive LLMs due to potential hallucinations. To handle those challenges, we propose TOP-Training, a target-oriented pre-training paradigm that stands out among all domain adaptation techniques with two desirable features: (i) TOP-Training moves one step further than popular domain-oriented fine-tuning since it not only moves closer to the target domain, but also familiarizes itself with the target dataset, and (ii) it does not assume the existence of a large set of unlabeled instances from the target domain. Specifically, for a target Medical-EQA dataset, we extract its entities and leverage large language models (LLMs) to generate synthetic texts containing those entities; we then demonstrate that pretraining on this synthetic text data yields better performance on the target Medical-EQA benchmarks. Overall, our contributions are threefold: (i) TOP-Training, a new pretraining technique to effectively adapt LLMs to better solve a target problem, (ii) TOP-Training has a wide application scope because it does not require the target problem to have a large set of unlabeled data, and (iii) our experiments highlight the limitations of autoregressive LLMs, emphasizing TOP-Training as a means to unlock the true potential of bidirectional LLMs.

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