Summary of Effects Of Different Prompts on the Quality Of Gpt-4 Responses to Dementia Care Questions, by Zhuochun Li et al.
Effects of Different Prompts on the Quality of GPT-4 Responses to Dementia Care Questions
by Zhuochun Li, Bo Xie, Robin Hilsabeck, Alyssa Aguirre, Ning Zou, Zhimeng Luo, Daqing He
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates how different prompts affect the quality of responses generated by large language models (LLMs) in the healthcare domain of dementia caregiving. The researchers developed a novel prompt template with three components, which they used to generate 36 responses to real-world questions about dementia caregivers’ challenges. They compared the word count and response quality using a rating scale with five indicators. This exploratory study aims to fill the gap in understanding prompts’ effects on response quality in healthcare domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can generate helpful answers, but we don’t know how different questions affect their responses. In this research, scientists looked at what happens when they use special prompts to ask a big language model about dementia caregivers. They created new ways of asking the same question and tested which ones worked best. This study helps us understand how to get better answers from these models. |
Keywords
» Artificial intelligence » Language model » Prompt
