Summary of Retrospective Learning From Interactions, by Zizhao Chen et al.
Retrospective Learning from Interactions
by Zizhao Chen, Mustafa Omer Gul, Yiwei Chen, Gloria Geng, Anne Wu, Yoav Artzi
First submitted to arxiv on: 17 Oct 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 introduces ReSpect, a method for large language models (LLMs) to learn from implicit feedback signals in multi-turn interactions. By analyzing user signals such as rephrasing requests or expressing frustration, LLMs can identify task-independent patterns and improve their performance without requiring additional annotations. The authors demonstrate the effectiveness of ReSpect by deploying it in a multimodal interaction scenario where humans instruct an LLM to solve an abstract reasoning task with a combinatorial solution space. Results show that ReSpect improves the task completion rate from 31% to 82% through thousands of interactions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ReSpect is a new way for computers to learn from people’s feedback without needing extra help. When humans talk to large language models, they often give clues about what’s wrong or what they want. These hints are hidden in the conversation and can be used by the computer to get better at understanding what people mean. In this paper, the authors show how ReSpect helps a computer model solve a tricky problem that requires combining different ideas. By learning from feedback over thousands of interactions, the model gets much better at completing the task. |
