Summary of Circuit Compositions: Exploring Modular Structures in Transformer-based Language Models, by Philipp Mondorf et al.
Circuit Compositions: Exploring Modular Structures in Transformer-Based Language Models
by Philipp Mondorf, Sondre Wold, Barbara Plank
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 explores the extent to which neural networks, particularly language models, implement reusable functions through subnetworks that can be composed to perform more complex tasks. Researchers have made progress in identifying “circuits” – minimal computational subgraphs responsible for a model’s behavior on specific tasks. However, most studies focus on individual tasks without investigating how functionally similar circuits relate to each other. This paper addresses this gap by analyzing circuits for highly compositional subtasks within a transformer-based language model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how neural networks work and what makes them able to do complex things. It’s like trying to figure out how a super-smart robot brain does its job! Researchers have already found some “circuits” that help the brain do specific tasks, but they haven’t looked at how these circuits are connected. This paper takes a step in that direction by studying how similar circuits work together within a special kind of language model. |
Keywords
» Artificial intelligence » Language model » Transformer
