Finetune multiple cognitive tasks with GPT-3 on medical texts (and reduce hallucination)

TL;DR

Exploring how GPT-3 can address confabulation in answering medical questions by fine-tuning prompts.

Transcript

good morning everybody david shapiro here with a new video about gpt 3. so in this one we are going to address a common problem that people have which is the gp gpt-3 there you go will make stuff up so this is called hallucinating or confabulation um really from a from a neurological standpoint it's actually confabulation because it's making up fac... Read More

Key Insights

• 😷 Confabulation in GPT-3 requires fine-tuning to recognize and address gaps in medical information.
• 😷 Training GPT-3 with specific prompts helps develop accurate medical question answering capabilities.
• 😷 Using a diverse set of medical texts for training data enhances GPT-3's understanding and response accuracy.
• 😷 Curie may not suffice for the complexity of medical questions, suggesting a need for advanced models like Davinci.

Questions & Answers

Q: What is confabulation in GPT-3 context?

Confabulation refers to the act of GPT-3 creating responses by inventing facts based on limited data points rather than hallucinating outright.

Q: How does the suggested approach aim to resolve the confabulation issue?

By training GPT-3 to recognize when it lacks information using specific prompts and responses, the model learns to acknowledge gaps in knowledge and give accurate answers.

Q: What was the challenge presented regarding a chatbot and patient information?

The challenge involved a chatbot generating inaccurate responses about medications in a patient's texts or graphs, highlighting the need for training GPT-3 to provide reliable medical insights.

Q: What insight did the speaker gain from using medical texts for training data?

The speaker realized that training with a diverse range of medical texts, even if a small subset is necessary, is crucial for GPT-3 to learn to identify medical concepts and provide accurate responses.

Summary & Key Takeaways

• GPT-3 confabulates answers based on limited data points in medical contexts, requiring fine-tuning for accurate responses.

• Explanation of how GPT-3 can improve medical question answering by training to recognize gaps in information.

• Demonstrating the process of generating medical text prompts, obtaining GPT-3 responses, and fine-tuning for accuracy.