ML models can perform a range of tasks and subtasks, some of which are more closely related to one another than are others. In this post, we set out two very initial starting points. First, we motivate reverse engineering models’ task decompositions. We think this can be helpful for interpretability and for understanding generalization. Second, we provide a (potentially non-exhaustive, initial) list of techniques that could be used to quantify the ‘distance’ between two tasks or inputs. We hope these distances might help us identify the task decomposition of a particular model. We close by briefly considering analogues in humans and by suggesting a toy model.

Epistemic status: We didn’t spend much time writing this post. Please let us know in the comments if you have other ideas for measuring task distance or if we are replicating work.

Introduction.

It might be useful to think about [...]

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Outline:

(02:03) Why understanding task structure could be useful

(02:08) Interpretability

(03:05) Learning the abstractions

(03:47) Unlearning capabilities

(04:46) Quantifying generalization

(05:58) Learning how the world works

(06:22) Some Subtleties

(06:26) What is a task?

(07:59) Task decomposition in the dataset vs a particular system's task decomposition

(08:47) Absolute vs relative metrics vs clusterings

(09:48) Methods for gauging task structure in ML

(09:57) Inspecting activations

(13:17) Inspecting learning

(15:56) Inspecting weights

(17:52) Analogues in humans

(18:57) A toy model for testing task decomposition techniques

(21:50) Acknowledgements

The original text contained 17 footnotes which were omitted from this narration.

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First published:
February 24th, 2024

Source:
https://www.lesswrong.com/posts/exp4JGPJu46g6sdRp/a-starting-point-for-making-sense-of-task-structure-in

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Narrated by TYPE III AUDIO.

“A starting point for making sense of task structure (in machine learning)” by Kaarel, RP, jake_mendel

LessWrong (30+ Karma)