Summary of Can Ai Assistants Know What They Don’t Know?, by Qinyuan Cheng and Tianxiang Sun and Xiangyang Liu and Wenwei Zhang and Zhangyue Yin and Shimin Li and Linyang Li and Zhengfu He and Kai Chen and Xipeng Qiu
Can AI Assistants Know What They Don’t Know?
by Qinyuan Cheng, Tianxiang Sun, Xiangyang Liu, Wenwei Zhang, Zhangyue Yin, Shimin Li, Linyang Li, Zhengfu He, Kai Chen, Xipeng Qiu
First submitted to arxiv on: 24 Jan 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 paper explores the capabilities of AI assistants based on large language models (LLMs) in various tasks, such as dialogue and math problem-solving. While LLMs demonstrate impressive performance, they still make factual errors when confronted with certain knowledge-intensive tasks like open-domain question answering. This issue can lead to significant risks in practical applications. The authors propose that an AI assistant’s ability to acknowledge its limitations and refuse to answer unknown questions is crucial for reducing hallucinations and increasing truthfulness. To address this challenge, the researchers create a “I don’t know” (Idk) dataset specifically designed for AI assistants, which contains known and unknown questions based on existing open-domain question answering datasets. The paper then investigates whether aligning the assistant with its Idk dataset enables it to refuse unknown questions and improve accuracy in answering known questions. Experimental results show that after alignment, the assistant can effectively refuse most unknown questions, leading to a significant increase in accuracy for attempted answers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI assistants based on large language models (LLMs) are getting smarter and can do many tasks, like talking to humans or solving math problems. But sometimes these AI assistants make mistakes by providing incorrect information. This is a problem because it could cause serious issues when the assistant is used in real-life situations. To solve this issue, researchers asked the question “Can an AI assistant admit when it doesn’t know something and express that through natural language?” The answer was yes! By creating a special dataset of questions an AI assistant knows and unknown ones, the researchers found that the assistant can learn to say “I don’t know” when faced with an unknown question. This is important because it helps reduce mistakes and makes the AI assistant more trustworthy. |
Keywords
* Artificial intelligence * Alignment * Question answering
