Summary of Cold: Causal Reasoning in Closed Daily Activities, by Abhinav Joshi and Areeb Ahmad and Ashutosh Modi
COLD: Causal reasOning in cLosed Daily activities
by Abhinav Joshi, Areeb Ahmad, Ashutosh Modi
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposed COLD framework, built upon human understanding of daily real-world activities, bridges the gap between symbolic representation-based question answering and open-ended causal reasoning. By creating enormous causal queries (~ 9 million), the framework simulates causal reasoning to evaluate the understanding of a daily real-world task. Multiple Large Language Models (LLMs) are evaluated on these queries, revealing that even trivial tasks for humans pose significant challenges for LLMs in causal reasoning. The study uses the backdoor criterion to determine the causal strength between events, providing insights into the intellectual capabilities of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can do many things, but can they really understand the world like humans do? To find out, researchers created a special kind of question-answering system called COLD. This framework uses real-world activities to help computers reason about causes and effects, just like we do. They asked thousands of questions and found that even simple tasks for us are hard for computers to get right. The study shows how well computers can understand the world by asking them questions about everyday things. |
Keywords
» Artificial intelligence » Question answering
