The Mechanistic and Doubtability of Attention Heads

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In the limited examples we have so far it turned out to be a pretty easy process but I expect there are definitely cases where it's much harder. If all goes according to plan we're gonna presumably see people starting to isolate a lot of subgraphs. i'm excited the c-work that provides evidence like either way for hopefully the interpretability of just these like just raw subgraphs of huge numbers of attention heads and nlp is all that they will be somehow misleading or confusing and not useful this would be a useful piece of evidence.

Arthur Conmy sits down with Nathan Labenz for an accessible deep dive into the state of interpretability research online today. They discuss how pioneering researchers have painstakingly worked to isolate the sub-circuits within transformers that are responsible for different aspects of AI capabilities. Arthur also introduces us to a new ACDC approach that he and his co-authors have taken to automating some of the most time-consuming parts of this work. If you’re looking for an ERP platform, check out our sponsor, NetSuite: http://netsuite.com/cognitive

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TIMESTAMPS:

(00:00) Episode Preview

(04:40) What attracted Arthur to mechanistic interpretability?

(07:49) LLM information processing: General Understanding vs Stochastic Parrot Paradigm

(14:00) ACDC paper: https://arxiv.org/abs/2304.14997

(14:45) Sponsors: NetSuite | Omneky

(24:30) Putting together data sets

(32:39) How to intervene in LLMs network activity

(36:00) Defining metrics to evaluate the production of correct completions

(44:20) The future of the mechanistic interpretability research

(50:00) Extracting upstream activations in the ACDC project and evaluating impact on downstream components.

(56:00) Anthropic research findings

(01:08:00) 3-Step process of the ACDC approach

(01:22:00) Setting a threshold and validation

(01:27:00) Goal of the approach

(01:32:00) Compute requirements

Correction: at (01:33:00), Arthur meant to say = "quadratic in nodes"

(01:35:30) Scaling laws for mechanistic interpretability

(01:40:00) Accessibility of this research for casual enthusiasts

(01:46:00) Emergence discourse

(01:56:00) Path to AI safety

LINKS:

https://arthurconmy.github.io/

https://arxiv.org/abs/2304.14997

@labenz (Nathan)

@arthurconmy (Arthur)

@cogrev_podcast

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