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Circuits Updates: July 2024 (Transformer Circuits Thread)
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This is a periodic update from Anthropic's circuits team, useful for researchers tracking mechanistic interpretability progress; read alongside earlier Transformer Circuits Thread papers for full context.
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Summary
A progress update from Anthropic's transformer circuits research team, summarizing recent findings and advances in mechanistic interpretability of neural networks. The update covers ongoing work to understand the internal computations of transformer models at a circuit level. It serves as a research communication bridging formal papers with ongoing experimental work.
Key Points
- •Provides incremental updates on mechanistic interpretability research focused on understanding transformer circuits
- •Part of the ongoing Transformer Circuits Thread series, which aims to reverse-engineer neural network computations
- •Likely covers advances in superposition, features, and attention head analysis within large language models
- •Serves as a living document connecting prior circuit-level discoveries to new experimental findings
- •Contributes to Anthropic's broader interpretability agenda aimed at making AI systems more transparent and safe
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[Transformer Circuits Thread](https://transformer-circuits.pub/)
# Circuits Updates - July 2024
### Authors
### Affiliations
### Published
_Not published yet._
### DOI
_No DOI yet._
We report a number of developing ideas on the Anthropic interpretability team, which might be of interest to researchers working actively in this space. Some of these are emerging strands of research where we expect to publish more on in the coming months. Others are minor points we wish to share, since we're unlikely to ever write a paper about them.
We'd ask you to treat these results like those of a colleague sharing some thoughts or preliminary experiments for a few minutes at a lab meeting, rather than a mature paper.
New Posts
- [The Next Five Hurdles](https://transformer-circuits.pub/2024/july-update/index.html#hurdles)
- [What is a Linear Representation? What is a Multidimensional Feature?](https://transformer-circuits.pub/2024/july-update/index.html#linear-representations)
- [The Dark Matter of Neural Networks?](https://transformer-circuits.pub/2024/july-update/index.html#dark-matter)
- [Attention Pivot Tables](https://transformer-circuits.pub/2024/july-update/index.html#pivot-tables)
- [Measuring feature sensitivity using dataset filtering](https://transformer-circuits.pub/2024/july-update/index.html#feature-sensitivity)
* * *
## [The Next Five Hurdles](https://transformer-circuits.pub/2024/july-update/index.html\#hurdles)
Chris Olah; edited by Adam Jermyn
If you'd asked me a year ago what the key open problems for mechanistic interpretability were, I would have told you the most important problem was superposition.
1
This might have been followed by the challenge of scalability: even if we can decompose very large models into understandable pieces, how can we turn that into understanding the model as a whole? Then I might have listed attention superposition as a very tertiary challenge. Since then, very significant progress has been made on superposition (e.g. [Bricken](https://transformer-circuits.pub/2023/monosemantic-features/index.html) [et al.](https://transformer-circuits.pub/2023/monosemantic-features/index.html), [Cunningham](https://arxiv.org/pdf/2309.08600) [et al.](https://arxiv.org/pdf/2309.08600), [Templeton](https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html)[et al.](https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html), [Gao](https://arxiv.org/pdf/2406.04093) [et al.](https://arxiv.org/pdf/2406.04093)). As a result, it feels like a good moment to take stock and ask "what are the remaining hurdles between us and having a mechanistic understanding of neural networks?" From my personal perspective, a few hurdles stand out as major challenges we need to confront.
Before we dive in, it's worth describing the path I'm imagining us following, such that we'll run into these hurdles. Roughly, I imagine:
- We find interpretable features which are the ["variables" of the computation](https://t
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