{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/quality-quantity-synthetic-corpora-from","title":"TOP-Training: Target-Oriented Pretraining for Medical Extractive Question Answering","arxiv_id":"2310.16995","date":"2023-10-25","proceeding":null,"authors":["Saptarshi Sengupta","Connor Heaton","Shreya Ghosh","Wenpeng Yin","Preslav Nakov","Suhang Wang"],"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.","url_abs":"https://arxiv.org/abs/2310.16995v2","url_pdf":"https://arxiv.org/pdf/2310.16995v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"quality-quantity-synthetic-corpora-from","repo_url":"https://github.com/saptarshi059/cdqa-v1-targetted-pretraining","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"extractive-question-answering","task_name":"Extractive Question-Answering"},{"task_slug":"knowledge-distillation","task_name":"Knowledge Distillation"},{"task_slug":"philosophy","task_name":"Philosophy"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"specificity","task_name":"Specificity"}],"methods":[{"method_slug":"align","method_name":"ALIGN"},{"method_slug":"galactica","method_name":"Galactica"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}