CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering
Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts. Studies show that neural QA models trained on one corpus may not generalize well to new clinical texts from a different institute or a different patient group, where large-scale QA pairs are not readily available for model retraining. To address this challenge, we propose a simple yet effective framework, CliniQG4QA, which leverages question generation (QG) to synthesize QA pairs on new clinical contexts and boosts QA models without requiring manual annotations. In order to generate diverse types of questions that are essential for training QA models, we further introduce a seq2seq-based question phrase prediction (QPP) module that can be used together with most existing QG models to diversify the generation. Our comprehensive experiment results show that the QA corpus generated by our framework can improve QA models on the new contexts (up to 8% absolute gain in terms of Exact Match), and that the QPP module plays a crucial role in achieving the gain.
Code (2)
Tasks
Domain AdaptationQuestion AnsweringQuestion GenerationQuestion-GenerationSimilar Papers 제목 키워드 기반
Domain-Specific Data Generation Framework for RAG Adaptation
Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning power of large language models (LLMs) with external retrieval to enable domain-grounded responses. Effectively adapting RAG systems t…
NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions
Scaling reasoning capabilities beyond traditional domains such as math and coding is hindered by the lack of diverse and high-quality questions. To overcome this limitation, we introduce a scalable approach for generatin…
Knowledge DistillationMathDiversity Enhanced Narrative Question Generation for Storybooks
Question generation (QG) from a given context can enhance comprehension, engagement, assessment, and overall efficacy in learning or conversational environments. Despite recent advancements in QG, the challenge of enhanc…
DiversityQuestion AnsweringQuestion GenerationQuestion-GenerationAnswering Ambiguous Questions with a Database of Questions, Answers, and Revisions
Many open-domain questions are under-specified and thus have multiple possible answers, each of which is correct under a different interpretation of the question. Answering such ambiguous questions is challenging, as it …
Passage RetrievalQuestion AnsweringRetrievalOn Correlating Factors for Domain Adaptation Performance
Dense retrievers have demonstrated significant potential for neural information retrieval; however, they lack robustness to domain shifts, limiting their efficacy in zero-shot settings across diverse domains. In this pap…
Domain AdaptationInformation RetrievalRetrieval