Back
Circuits Updates: July 2024 (Transformer Circuits Thread)
webCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: Transformer Circuits
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.
Metadata
Importance: 55/100blog postprimary source
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
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Anthropic Core Views | Safety Agenda | 62.0 |
Cached Content Preview
HTTP 200Fetched Apr 9, 202640 KB
Circuits Updates - July 2024
Transformer Circuits Thread
Circuits Updates - July 2024
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
What is a Linear Representation? What is a Multidimensional Feature?
The Dark Matter of Neural Networks?
Attention Pivot Tables
Measuring feature sensitivity using dataset filtering
The Next Five 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 . 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 et al. , Cunningham et al. , Templeton et al. , Gao et al. ). 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 we're interested in.
We understand the circuits that compute them.
We somehow turn this microscopic understanding of neural networks into a macroscopic picture addressing the questions we care about.
Following this path, I see five additional hurdles in our future:
The Missing Features. Historically, the first step of our path – find the interpretable features – was significantly blocked by superposition. It's now substantially unblocked: we can automatically extract large numbers of interpretable features. However, it seems that we are likely only extracting a small fraction of the features. There may be an enormous number of rare features we can't yet extract. Without major algorithmic innovation, it's possible we'll never be able to resolve the rarest features, leaving us with a kind of neural network dark matter .
Cross-Layer Superposition. Consider a model with 100 layers. If the circuits the model is trying to implemen
... (truncated, 40 KB total)Resource ID:
b0b05dd056f72fe0 | Stable ID: sid_3bq9VN4xBA