Summary of Rethinking Interpretability in the Era Of Large Language Models, by Chandan Singh et al.
Rethinking Interpretability in the Era of Large Language Models
by Chandan Singh, Jeevana Priya Inala, Michel Galley, Rich Caruana, Jianfeng Gao
First submitted to arxiv on: 30 Jan 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 In this paper, researchers explore the intersection of interpretable machine learning and large language models (LLMs). As LLMs have shown remarkable abilities across various tasks, they offer a chance to rethink opportunities in interpretable machine learning. The authors highlight how these new capabilities raise challenges like hallucinated explanations and immense computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making sure the machines we use can explain what they’re doing. It’s like when you ask Siri or Alexa to do something, and they tell you why they did it that way. Well, this paper talks about how big language models are getting better at doing that too! But with great power comes great responsibility – these new capabilities also bring some big challenges. |
Keywords
* Artificial intelligence * Machine learning
