RSS-Feed abonnieren
DOI: 10.1055/s-0044-1782165
Response Generated by Large Language Models Depends on the Structure of the Prompt
Funding None.We appreciate the opportunity to respond to the letter written in response to our published article titled “Assessing the capability of ChatGPT, Google Bard, and Microsoft Bing in solving radiology case vignettes.”[1] We thank the authors for their interest in our work and thoughtful questions.
The use of large language models (LLMs) like ChatGPT, Google Bard, Microsoft Bing, etc., in radiology is indeed a burgeoning field, and we are pleased that our study has sparked further discussion. We would like to address the raised concerns and provide additional information to enhance the clarity of our methodology.
The authors asked us about the input prompts. We used the Fellowship of the Royal College of Radiologists Part 2A pattern questions directly as prompts. There was no prefix (e.g., role definition) or suffix (e.g., customizing response for reader) attached to the prompt. [Fig. 1] present such a question used as a prompt in ChatGPT (GPT3.5; free version).
We appreciate the authors' acknowledgment of the potential influence of prompt engineering on the performance of LLMs. Indeed, the intricacies of prompt design can change the response drastically. We summarized top ten tips in [Table 1] about designing prompts for better output from the LLM. There are several websites that help training prompt engineering.[2] [3]
Note: A guide is also available from https://platform.openai.com/docs/guides/prompt-engineering/six-strategies-for-getting-better-results.
The authors raise a valid question about any training we used for the chatbots. We confirm that the chatbots, including ChatGPT, Google Bard, and Microsoft Bing, were not trained by us. Instead, we utilized pretrained models from the respective platforms to ensure a fair evaluation of their out-of-the-box diagnostic capabilities. However, studies have reported that fine-tuned GPT 3.5 and GPT 4 models have shown better responses.[4]
We hope that these clarifications address the concerns raised by the authors. Thank you for providing this platform for academic discourse.
Publikationsverlauf
Artikel online veröffentlicht:
25. März 2024
© 2024. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Thieme Medical and Scientific Publishers Pvt. Ltd.
A-12, 2nd Floor, Sector 2, Noida-201301 UP, India