Summary of An Evaluation Of Estimative Uncertainty in Large Language Models, by Zhisheng Tang et al.
An Evaluation of Estimative Uncertainty in Large Language Models
by Zhisheng Tang, Ke Shen, Mayank Kejriwal
First submitted to arxiv on: 24 May 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 the ability of large language models (LLMs) such as GPT-4 and ERNIE-4 to express estimative probability (WEPs), like “maybe” or “probably not”, similar to humans. The researchers compare the LLM’s performance with human estimates on WEPs in English, as well as in gendered roles and Chinese contexts. They find that while some LLMs align with human estimates, others diverge, particularly when presented with complex linguistic structures. Moreover, they observe a significant gap between the statistical uncertainty of LLMs and their ability to map between statistical and estimative uncertainty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well big language models can express uncertain opinions like “maybe” or “probably not”. It compares these models to people’s opinions on similar topics. The researchers find that some models do a good job, but others don’t quite match up with human opinion. They also see that the models struggle when dealing with complex sentences and cultural nuances. Overall, this study helps us understand how well computers can mimic human thought. |
Keywords
» Artificial intelligence » Gpt » Probability
