用微调的RoBERTa识别医学摘要术语，结合大模型完成全文通俗化改写。
MaLei at the PLABA Track of TREC 2024: RoBERTa for Term Replacement -- LLaMA3.1 and GPT-4o for Complete Abstract Adaptation
- 用微调RoBERTa-Base识别医学术语与缩写，精准定位难懂词汇。
- 在术语替换任务中分别获第3和第2名，平均F1排名第一。
- 使用LLaMA3.1和GPT-4o实现全文通俗化，完整度得分最高。
本文介绍曼彻斯特与莱顿大学团队（原BeeManc）在TREC2024会议中的PLABA共享任务系统。该任务分为两项子任务：任务一（术语替换）采用微调的ReBERTa-Base模型识别生物医学摘要中的难点术语、行话与缩写，并报告了任务1A与1B的F1分数；任务二（全文改写）则利用LLaMA3.1-70B-Instruct与GPT-4o，通过单次提示完成摘要通俗化改写，并评估了BLEU、SARI、BERTScore、LENS与SALSA得分。根据官方结果，我们的小型微调模型在任务1A与1B上分别排名第三与第二，两项任务平均F1分位列9个参评系统首位；而LLaMA3.1-70B-Instruct模型在任务2中取得最高完整度得分。相关代码与模型已开源至https://github.com/HECTA-UoM/PLABA2024。
原文摘要 · Abstract (English)
This report is the system description of the MaLei team (Manchester and Leiden) for the shared task Plain Language Adaptation of Biomedical Abstracts (PLABA) 2024 (we had an earlier name BeeManc following last year), affiliated with TREC2024 (33rd Text REtrieval Conference https://ir.nist.gov/evalbase/conf/trec-2024). This report contains two sections corresponding to the two sub-tasks in PLABA-2024. In task one (term replacement), we applied fine-tuned ReBERTa-Base models to identify and classify the difficult terms, jargon, and acronyms in the biomedical abstracts and reported the F1 score (Task 1A and 1B). In task two (complete abstract adaptation), we leveraged Llamma3.1-70B-Instruct and GPT-4o with the one-shot prompts to complete the abstract adaptation and reported the scores in BLEU, SARI, BERTScore, LENS, and SALSA. From the official Evaluation from PLABA-2024 on Task 1A and 1B, our much smaller fine-tuned RoBERTa-Base model ranked 3rd and 2nd respectively on the two sub-tasks, and the 1st on averaged F1 scores across the two tasks from 9 evaluated systems. Our LLaMA-3.1-70B-instructed model achieved the highest Completeness score for Task 2. We share our source codes, fine-tuned models, and related resources at https://github.com/HECTA-UoM/PLABA2024
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。