Summary of Follow-up Questions Improve Documents Generated by Large Language Models, By Bernadette J Tix
Follow-Up Questions Improve Documents Generated by Large Language Models
by Bernadette J Tix
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The study explores the impact of Large Language Models (LLMs) generating follow-up questions in response to user requests for short text documents. A novel web-based AI system asks users follow-up questions to clarify their needs or offer additional insights before generating the requested document. Users are shown two versions: one generated using both the initial request and the questions, and another using only the initial request. They indicate which they prefer and provide feedback on the question-answering process. The findings show clear benefits of question-asking in terms of document preference and user experience. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how AI-generated follow-up questions can improve our interaction with AI systems. By asking users questions, the AI system can generate better documents that are more relevant to their needs. This is especially true when the questions are thought-provoking or offer new insights. The users in this study found these kinds of questions more valuable than simple information-gathering ones. |
Keywords
» Artificial intelligence » Question answering
