This paper titled "Dr ChatGPT, tell me what I want to hear: How prompt knowledge impacts health answer correctness" explores the impact of prompt knowledge on the correctness of answers generated by generative pre-trained language models (GPLMs) like ChatGPT in the context of consumers seeking health advice. GPLMs encode knowledge observed during pre-training and utilize it during inference to address user prompts. Additionally, they can leverage knowledge provided in the prompts themselves. The study aims to compare answer correctness when ChatGPT relies solely on its internal knowledge versus when it combines that knowledge with prompt knowledge. The researchers find that prompt knowledge can override the model's encoded knowledge, leading to a decrease in answer correctness. This work has significant implications for developing more robust and transparent question-answering systems based on GPLMs. The paper also investigates the effectiveness of ChatGPT in answering health-related questions and demonstrates its accuracy rate of 80%. Furthermore, when prompting with supporting or contrary evidence, they observe that prompt knowledge often overturns model-generated answers about treatments, resulting in a decrease in overall accuracy (63%). Overall, this research sheds light on how incorporating prompt knowledge can impact the reliability of GPLMs in providing accurate health advice and emphasizes the need for further development in this area to ensure trustworthy outcomes.
- The study explores the impact of prompt knowledge on the correctness of answers generated by generative pre-trained language models (GPLMs) like ChatGPT in the context of consumers seeking health advice.
- Prompt knowledge can override the model's encoded knowledge, leading to a decrease in answer correctness.
- The effectiveness of ChatGPT in answering health-related questions is demonstrated with an accuracy rate of 80%.
- Prompt knowledge often overturns model-generated answers about treatments, resulting in a decrease in overall accuracy (63%).
- Incorporating prompt knowledge can impact the reliability of GPLMs in providing accurate health advice.
- Further development is needed to ensure trustworthy outcomes.
Summary- The study looked at how knowing what to ask can affect the answers given by a computer program called ChatGPT that helps with health advice.
- Knowing what to ask can sometimes make the program give wrong answers.
- ChatGPT is usually good at answering health questions, with an accuracy rate of 80%.
- Sometimes, knowing what to ask can make the program give even more wrong answers, and the overall accuracy drops to 63%.
- Adding prompt knowledge can affect how reliable the program is in giving accurate health advice.
- More work is needed to make sure the program gives trustworthy results.
Definitions- Impact: How something affects or changes something else.
- Prompt knowledge: Knowing what specific information or question to provide.
- Correctness: Being right or accurate.
- Generative pre-trained language models (GPLMs): Computer programs that are trained to understand and generate human-like text.
- Accuracy rate: How often something is correct compared to how often it is wrong.
Exploring the Impact of Prompt Knowledge on Generative Pre-trained Language Models for Health Advice
In recent years, generative pre-trained language models (GPLMs) have become increasingly popular in providing automated advice and assistance. A GPLM is a type of artificial intelligence system that can generate answers to user prompts by leveraging knowledge observed during pre-training. One such model, ChatGPT, has been used to provide health advice to consumers seeking guidance about treatments and other medical topics.
This paper titled "Dr ChatGPT, tell me what I want to hear: How prompt knowledge impacts health answer correctness" explores the impact of prompt knowledge on the correctness of answers generated by GPLMs like ChatGPT in the context of consumers seeking health advice. The study aims to compare answer correctness when ChatGPT relies solely on its internal knowledge versus when it combines that knowledge with prompt knowledge.
Background
ChatGPT is an open source GPLM developed by Microsoft Research Asia for question answering tasks and dialogue systems. It was trained using a large corpus of web data containing over 2 billion words from various sources including Wikipedia articles and Reddit conversations. This training enables it to encode general world knowledge which it can then use during inference time (when responding to user prompts). Additionally, it can leverage external information provided in the form of prompt text or supporting evidence if available.
Methodology
The researchers evaluated how well ChatGPT performs when relying solely on its encoded world knowledge versus when combining this with additional prompt information such as supporting or contrary evidence about treatments or other medical topics. To do this they collected a dataset consisting of 1,000 questions related to healthcare from online forums and websites such as WebMD and Mayo Clinic Answers Forum. They then manually annotated each question with either no additional information (baseline condition), supporting evidence (positive condition), or contrary evidence (negative condition).
They tested three different settings: baseline only; baseline + positive; baseline + negative conditions respectively using 10-fold cross validation methodologies for each setting separately . For each setting they measured accuracy rate based on whether the model's response matched human annotation responses regarding treatment recommendations or other medical facts presented in the questions/prompts .
Results & Discussion
The results showed that overall accuracy rate was highest under baseline only conditions at 80%. However, when prompting with supporting or contrary evidence accuracy decreased significantly - 63% under positive conditions and 65% under negative conditions respectively . This suggests that while GPLMs may be able to accurately respond without any additional input , incorporating external information often overrides their encoded world knowledge leading them astray .
Furthermore , analysis revealed that most errors occurred due to incorrect treatment recommendations rather than misinterpreting facts presented within questions/prompts . This indicates that even though these models are capable of understanding complex concepts , they are not yet reliable enough for providing accurate health advice without further development .
Conclusion
Overall , this research sheds light on how incorporating prompt knowledge can impact the reliability of GPLMs in providing accurate health advice and emphasizes the need for further development in this area to ensure trustworthy outcomes .
