Summary of Perception Of Knowledge Boundary For Large Language Models Through Semi-open-ended Question Answering, by Zhihua Wen et al.
Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering
by Zhihua Wen, Zhiliang Tian, Zexin Jian, Zhen Huang, Pei Ke, Yifu Gao, Minlie Huang, Dongsheng Li
First submitted to arxiv on: 23 May 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 This paper investigates the knowledge boundary (KB) of Large Language Models (LLMs), which limits their factual understanding and can lead to hallucinations. The authors focus on semi-open-ended questions (SoeQ) that require LLMs to generate multiple potential answers, as opposed to close-ended questions with a single correct answer. To perceive the KB of LLMs, they develop an approach that constructs SoeQ, obtains answers from a target LLM, and estimates the probabilities of existing answers using an open-sourced auxiliary model. The results show that GPT-4 performs poorly on SoeQ and is often unaware of its KB. The authors categorize four types of ambiguous answers beyond the KB of the target LLM and create a dataset to perceive the KB for GPT-4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Language Models (LLMs) can understand questions that have many possible answers. Right now, we test these models using easy questions with one correct answer, but this doesn’t help us figure out when they might start making things up. The authors of the paper want to change this by creating a new way to ask questions and checking what answers the LLMs come up with. They use a special tool to make the questions and see what answers the models give. Then, they try to understand why some answers are more likely than others. This helps them discover when the models don’t know something and might start making things up. |
Keywords
» Artificial intelligence » Gpt
