Summary of Analobench: Benchmarking the Identification Of Abstract and Long-context Analogies, by Xiao Ye et al.
AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies
by Xiao Ye, Andrew Wang, Jacob Choi, Yining Lu, Shreya Sharma, Lingfeng Shen, Vijay Tiyyala, Nicholas Andrews, Daniel Khashabi
First submitted to arxiv on: 19 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 This paper explores the ability of language models (LMs) to engage in analogical thinking, a crucial human cognitive skill. To evaluate this capability, the authors propose AnaloBench, a benchmark that assesses LMs’ capacity for recalling related experiences and applying analogical reasoning to complex scenarios. The study tests various proprietary and open-source models, including GPT family members and LLaMA2. Surprisingly, the results show that scaling up LMs has minimal impact when dealing with lengthy analogies or large information pools, highlighting the need for further research in this area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at whether computers can think like humans do when we make connections between ideas. This is called analogical thinking and helps us solve problems, understand hard concepts, and explain our ideas better. The researchers created a test called AnaloBench to see how well computer models can do this. They tested many different models, including some really good ones like GPT and LLaMA2. What they found was that making computers think more doesn’t always help when we’re trying to find connections between big ideas or lots of information. |
Keywords
» Artificial intelligence » Gpt
