Summary of Human-like Category Learning by Injecting Ecological Priors From Large Language Models Into Neural Networks, By Akshay K. Jagadish et al.
Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks
by Akshay K. Jagadish, Julian Coda-Forno, Mirko Thalmann, Eric Schulz, Marcel Binz
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 proposed approach uses large language models to generate cognitive tasks that mimic real-world category learning tasks, addressing the challenge of defining ecological validity. By leveraging meta-learning and a framework called ecologically rational meta-learned inference (ERMI), researchers can derive rational agents adapted to these tasks. The ERMI model is tested against seven other cognitive models in two experiments, demonstrating improved performance and qualitative matches with human behavior. Additionally, ERMI achieves state-of-the-art results on the OpenML-CC18 classification benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are used to create category learning tasks that match real-world statistics. The research uses a framework called ecologically rational meta-learned inference (ERMI) to develop rational agents adapted to these tasks. The ERMI model is tested against other cognitive models and performs well, matching human behavior in several ways. This research can help us better understand how humans learn and make decisions. |
Keywords
* Artificial intelligence * Classification * Inference * Meta learning
