Summary of Cogen: Learning From Feedback with Coupled Comprehension and Generation, by Mustafa Omer Gul et al.
CoGen: Learning from Feedback with Coupled Comprehension and Generation
by Mustafa Omer Gul, Yoav Artzi
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 This paper proposes techniques for tightly integrating language comprehension and generation capabilities within systems that learn from interaction with users. The focus is on continually learning from user feedback signals in two-player reference games. Various models are deployed for thousands of interactions, leading to dramatic performance improvements over time. Comprehension-generation coupling results in up to 26% absolute improvement and up to 17% higher accuracies compared to a non-coupled system. The analysis also reveals that coupling has a substantial qualitative impact on the system’s language, making it more human-like. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching machines to understand and generate language better by letting them play games with humans. The idea is that as the machine plays, it can learn from what the humans say and do, getting smarter and more like a human over time. The researchers tested this idea in a game where two players take turns answering questions, and they found that when the machine could both understand and generate language, it got much better at playing the game than if it only understood or generated language. This is important because it means machines could one day have conversations with humans that feel more natural and friendly. |
