Abstract
In this paper, we present our system applied in BioASQ 11b phase b. We showcase prompt engineering strategies and outline our experimental steps. Building upon the success of ChatGPT/GPT-4 in answer generation and the field of biology, we developed a system that utilizes GPT-4 to answer biomedical questions. The system leverages OpenAI’s ChatCompletions API and combines Prompt Engineering methods to explore various prompts. In addition, we also attempted to incorporate GPT-4 into our system from last year, which combines a BERT-based model and BERTScore. However, the standalone GPT-4 method outperformed this approach by a large margin. Ultimately, in our submission, we adopted what we believe to be the optimal prompts and achieved the highest scores in the second batch.
| Original language | English |
|---|---|
| Pages (from-to) | 114-121 |
| Number of pages | 8 |
| Journal | CEUR Workshop Proceedings |
| Volume | 3497 |
| State | Published - 2023 |
| Event | 24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023 - Thessaloniki, Greece Duration: 18 Sep 2023 → 21 Sep 2023 |
Keywords
- Biomedical Question Answer
- Generative Pre-trained Transformer
- Large language models (LLMs)
- Zero-shot