Summary of What Is It For a Machine Learning Model to Have a Capability?, by Jacqueline Harding et al.
What is it for a Machine Learning Model to Have a Capability?
by Jacqueline Harding, Nathaniel Sharadin
First submitted to arxiv on: 14 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 investigates the capabilities of machine learning (ML) models and their evaluation in modern ML research. The authors examine what it means to say a model is capable of doing something, drawing from philosophical literature on abilities. They propose a conditional analysis of model abilities (CAMA), which assesses a model’s capability to perform a task by considering its reliability in achieving the task if it “tries”. The paper operationalizes CAMA for large language models (LLMs) and demonstrates how it can be applied to ML model evaluation practice, enabling fair comparisons between models. By developing a framework for evaluating ML model capabilities, this research contributes to the burgeoning field of model evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about understanding what machine learning models can do and how we evaluate them. It’s important because there are many applications of these models in our daily lives, from language translation to medical diagnosis. The authors explore what it means when someone says a model is capable of doing something, using ideas from philosophy. They propose a new way to think about this called the conditional analysis of model abilities (CAMA). CAMA looks at whether a model would be successful if it tried to do something. The paper shows how CAMA can help us understand and compare different machine learning models. |
Keywords
» Artificial intelligence » Machine learning » Translation
