Summary of A Comprehensive Evaluation on Event Reasoning Of Large Language Models, by Zhengwei Tao et al.
A Comprehensive Evaluation on Event Reasoning of Large Language Models
by Zhengwei Tao, Zhi Jin, Yifan Zhang, Xiancai Chen, Haiyan Zhao, Jia Li, Bing Liang, Chongyang Tao, Qun Liu, Kam-Fai Wong
First submitted to arxiv on: 26 Apr 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 investigates the abilities of Large Language Models (LLMs) in performing event reasoning tasks, which is a crucial skill for various applications. The authors introduce a novel benchmark called EV2, which comprehensively evaluates LLMs’ event reasoning capabilities across different relations and reasoning paradigms. The results show that while LLMs can perform event reasoning, their performances are far from satisfactory, with an imbalance in their abilities. Furthermore, the study finds that LLMs have event schema knowledge, but it is not aligned with human utilization of this knowledge. To address these findings, the authors suggest guiding LLMs to utilize their event schema knowledge as memory, leading to improvements in event reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are very good at understanding and generating text. One important skill they need to have is something called “event reasoning”. This means they can understand events happening in a sequence and how they’re connected. But scientists didn’t know how well LLMs could do this on different kinds of connections between events and with different ways of thinking about those events. So, they created a special test to see how good LLMs are at event reasoning. The results showed that while LLMs can do it, they’re not very good at it yet. They also found out that LLMs have some knowledge about what kinds of events happen, but it’s different from how humans think about those events. To help LLMs get better at event reasoning, scientists suggest teaching them to use this knowledge as a kind of memory. |
