“Towards data-centric interpretability with sparse autoencoders” by Nick Jiang, lilysun004, lewis smith, Neel Nanda

Nick and Lily are co-first authors on this project. Lewis and Neel jointly supervised this project.

TL;DR

• We use sparse autoencoders (SAEs) for four textual data analysis
  tasks—data diffing, finding correlations, targeted clustering, and retrieval.
• We care especially about gaining insights from language model data, such as LLM outputs and training data, as we believe it is an underexplored route for model understanding.
• For instance, we find that Grok 4 is more careful than other frontier models to state its assumptions and explore nuanced interpretations—showing the kinds of insights data diffing can reveal by comparing model outputs.
• Why SAEs? Think of features as "tags" of properties for each text.
  • Their large dictionary of latents provides a large hypothesis space, enabling the discovery of novel insights (diffing and correlations).
  • SAEs capture more than just semantic information, making them effective alternatives to traditional embeddings when we want to find [...]

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Outline:

(00:22) TL;DR

(01:48) Introduction

(04:41) Preliminaries

(06:09) Data Diffing

(07:16) Identifying known differences from datasets

(09:09) Discovering novel differences between model behavior

(14:26) Correlations

(16:21) Finding known correlations

(17:45) Finding unknown correlations

(17:58) Finding bias in internet comments

(19:52) Finding patterns in model responses

(20:51) Clustering

(22:39) Discovering known clusters

(24:26) Discovering unknown clusters

(26:13) Retrieval

(33:45) Discussion and Limitations

(35:06) Awknowledgments

The original text contained 6 footnotes which were omitted from this narration.

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First published:

August 15th, 2025

Source:

https://www.lesswrong.com/posts/a4EDinzAYtRwpNmx9/towards-data-centric-interpretability-with-sparse

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Narrated by TYPE III AUDIO.

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