Summary of Empowering Language Models with Active Inquiry For Deeper Understanding, by Jing-cheng Pang et al.
Empowering Language Models with Active Inquiry for Deeper Understanding
by Jing-Cheng Pang, Heng-Bo Fan, Pengyuan Wang, Jia-Hao Xiao, Nan Tang, Si-Hang Yang, Chengxing Jia, Sheng-Jun Huang, Yang Yu
First submitted to arxiv on: 6 Feb 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 proposed LaMAI (Language Model with Active Inquiry) system aims to enhance the conversational abilities of large language models (LLMs) by incorporating active learning techniques. This approach allows LLMs to engage in dynamic bidirectional dialogues, refining their output and aligning it better with user expectations. By leveraging targeted questioning, LaMAI narrows the contextual gap between users’ queries and LLMs’ responses. Experimental results demonstrate the effectiveness of LaMAI, outperforming other leading question-answering frameworks on various complex datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LaMAI is a new way to make large language models work better with people. Right now, these models can be confused by what we mean when we ask them questions. To fix this, LaMAI helps the models ask follow-up questions to get more information. This makes the model’s answers more accurate and helpful. In tests, LaMAI did a great job of giving good answers, even better than other ways of asking questions. |
Keywords
» Artificial intelligence » Active learning » Language model » Question answering
