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Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet

Adly Templeton, Tom Conerly, Jonathan Marcus, Jack Lindsey, Trenton Bricken, Brian Chen, Adam Pearce, Craig Citro, Emmanuel Ameisen, Andy Jones, Hoagy Cunningham, Nicholas L Turner, Callum McDougall, Monte MacDiarmid, Alex Tamkin, Esin Durmus, Tristan Hume, Francesco Mosconi, C. Daniel Freeman, Theodore R. Sumers, Edward Rees, Joshua Batson, Adam Jermyn, Shan Carter, Chris Olah, Tom Henighan

arXiv:2605.29358v1cs.AI

TL;DR

The paper asks whether dictionary learning can scale beyond small language models and addresses this by training sparse autoencoders on Claude 3 Sonnet’s middle-layer residual stream. It finds interpretable features with abstract and multilingual structure that can support model steering, while acknowledging incomplete coverage and weak evaluation of computational faithfulness.

  • Problem

    The paper addresses the open question of whether dictionary learning methods developed on small language models generalize to large models.

  • Method

    The authors train sparse autoencoders on Claude 3 Sonnet’s normalized middle-layer residual-stream activations, using scaling laws to guide training choices.

  • Results

    The resulting features are interpretable, highly abstract, multilingual, multimodal, and usable for steering large-model behavior.

  • Takeaways & Limitations

    Unsupervised dictionary learning can uncover abstractions and associations in large models and features related to safety concerns.

  • Takeaways & Limitations

    The work lacks a clear ground-truth objective for evaluating whether the learned features faithfully capture model computations.

Abstract

from arXiv · show

We demonstrate that sparse autoencoders can extract interpretable features from Claude 3 Sonnet, a production-scale language model, addressing the open question of whether dictionary learning methods scale beyond small transformers. We trained sparse autoencoders with up to 34 million features on the model's middle layer residual stream, using scaling laws to guide hyperparameter selection. The resulting features are multilingual and multimodal (generalizing to images despite text-only training), respond to both concrete instances and abstract discussions of concepts, and can be used to steer model behavior in ways consistent with their interpretations. We find features corresponding to famous entities and locations, as well as more abstract concepts like sarcasm or errors in code. We also identify features relevant to ways in which language models might cause harm--including features representing deception, power-seeking, sycophancy, and bias--and show that these causally influence model outputs when manipulated. Additionally, we conduct analyses of feature interpretability, geometry, and computational function. However, significant limitations remain: our suite of features is incomplete, and we lack rigorous methods for evaluating whether our features faithfully capture model computations.

Key Results

The paper finds that sparse autoencoders produce interpretable features for large models, including abstract, multilingual, and steerable features linked to safety concerns.

  • Sparse autoencoders produce interpretable features for large models.
  • The resulting features are highly abstract, multilingual, multimodal, and generalize between concrete and abstract instantiations.
  • Feature dictionaries show a systematic relationship between concept frequency and the dictionary size needed to resolve features.
  • Features can be used to steer large models in ways consistent with their interpretations.
  • The features include representations related to safety concerns such as deception, sycophancy, and bias.

1 Scaling Dictionary Learning to Claude 3 Sonnet

The paper scales sparse autoencoder dictionary learning to Claude 3 Sonnet by decomposing middle-layer residual-stream activations into sparse feature directions. Scaling laws guide model design, while the resulting dictionaries achieve substantial reconstruction and reveal interpretable structure, though dead and degenerate features remain.

  • 1 Scaling Dictionary Learning to Claude 3 Sonnet: The central question is whether dictionary learning methods developed on small language models generalize to large models.
  • 1.1 Sparse Autoencoders: The encoder maps activations to a higher-dimensional feature layer with a learned linear transformation and ReLU, while the decoder reconstructs activations linearly.
  • 1 Scaling Dictionary Learning to Claude 3 Sonnet: Sparse autoencoders decompose model activations into a sparse linear combination of learned feature directions.
  • 1.2 Our SAE experiments: The study trains three sparse autoencoders with 1,048,576, 4,194,304, and 33,554,432 features, using scaling-law analysis to select training choices.
  • 1.2 Our SAE experiments: Fewer than 300 features were active on a given token on average, while reconstruction explained at least 65% of model-activation variance.
  • 1.2 Our SAE experiments: Dead features comprise roughly 2% of the 1M SAE, 35% of the 4M SAE, and 65% of the 34M SAE, with future training improvements expected to reduce them.
  • 1.3 Scaling Laws: Training loss decreases approximately as a power law with compute, and optimal training steps and feature counts also scale approximately as power laws.

2 Assessing Feature Interpretability

The paper evaluates whether Claude 3 Sonnet’s sparse-autoencoder features are interpretable, testing both their semantic specificity and their influence on model behavior. Across concrete concepts, code errors, functions, and automated comparisons with neurons, the results support feature interpretations while leaving their completeness and faithfulness unresolved.

  • Evaluation approach: The study tests feature interpretations through contextual specificity and interventions that examine whether activation changes downstream behavior.The authors distinguish reliable concept presence from causal influence on behavior.
  • Concrete features: 34M/31164353 activates primarily on Golden Gate Bridge references, while related monuments and tourist attractions produce weaker activations.The feature also responds to related bridges and attractions.
  • Concrete features: 34M/9493533 tracks neuroscience and related academic discussions, while other features represent transit infrastructure and popular tourist attractions.Examples include trains, ferries, tunnels, bridges, wormholes, the Eiffel Tower, and the Sistine Chapel.
  • Specificity: Strongly activating examples are judged highly consistent with proposed interpretations, although specificity decreases as activation strength weakens.The authors argue that the largest activations matter most for model behavior.
  • Influence on behavior: Feature steering modifies outputs in specific, interpretable ways, including demeanor, preferences, stated goals, biases, errors, and safeguards.The authors present this as evidence that feature interpretations align with how the model uses them.
  • Code-error features: Feature 1M/1013764 represents a broad variety of code errors, including incorrect names, invalid expressions, and invalid function inputs.It fires on similar bugs in C and Scheme but not on ordinary English typos, indicating code-context specificity.
  • Code-error features: The code-error feature’s manipulation causes the model to hallucinate an error message or rewrite code, linking activation to bug-related computation.Clamping the feature negatively causes the model to rewrite code without the prompt’s error.

3 Feature Survey

The feature survey examines the breadth, geometry, and coverage of Claude 3 Sonnet’s learned feature space. Larger dictionaries reveal finer-grained and previously absent concepts, but the discovered features remain incomplete and depend strongly on concept frequency in training data.

  • Feature breadth: The learned features span famous people, world locations, code type signatures, and many other semantic categories.The survey aims to characterize this breadth through manual inspection and automated interpretability.
  • Local geometry: Cosine similarity between feature vectors consistently identifies neighboring features with related meanings or contexts.The paper provides an interactive feature UMAP for exploring these neighborhoods.
  • Local geometry: Decoder-space distance roughly tracks conceptual relatedness, from San Francisco locations to nearby regions and more abstractly related tourist destinations.Examples include Alcatraz, the Presidio, Lake Tahoe, Yosemite, Solano County, Médoc, and the Isle of Skye.
  • Scaling feature dictionaries: Larger SAEs split broad features into multiple more specific features and can introduce concepts absent from smaller SAEs.A San Francisco feature splits into two 4M features and eleven fine-grained 34M features; earthquake features appear in larger SAEs without a 1M analogue.
  • Feature neighborhoods: The immunology neighborhood organizes features into clusters covering immunocompromised people, diseases, immune responses, organs, microscopic mechanisms, and techniques.The neighborhood also includes immunity-related features in legal and social contexts.
  • Feature neighborhoods: The Inner Conflict neighborhood contains subregions for tradeoffs, opposing principles and legal conflict, emotional struggle, reluctance, and guilt.Unlike the immunology neighborhood, it does not separate cleanly into clusters.
  • Coverage and completeness: Coverage increases with dictionary size, but the 34M SAE contains features for only about 60% of London boroughs despite Claude 3 Sonnet naming all boroughs.This suggests the model contains additional features that larger SAEs might extract.
  • Coverage and completeness: Feature representation is closely tied to concept frequency in the SAE training data, with larger SAEs reaching rarer concepts.The authors use this relationship to estimate the dictionary scale needed for concept-specific features.

4 Features as Computational Intermediates

Feature attributions and ablations identify interpretable features involved in Claude’s intermediate computations and next-token predictions. Case studies show features tracking emotional inferences and multi-step geographical reasoning, while raw activation rankings are often less informative.

  • Causal feature identification: Attributions approximate a feature’s effect on a next-token prediction, whereas ablations measure its full potentially nonlinear causal effect.Attributions are faster, so they are often used to filter features before ablation.
  • Causal feature identification: 0.8 correlation is observed between attribution and ablation effects when every active feature is ablated.
  • Computational intermediates: Features in the middle-layer residual stream are causally implicated in the model’s completion and can correspond to expected intermediate results.
  • Emotional inferences: In an emotional-inference prompt, a feature for wanting time alone activates from “alone” onward, suggesting the model has captured the statement’s gist.
  • Emotional inferences: A sadness-related feature activates on “John feels,” indicating an inferred emotional state from the preceding statement that someone wants to be alone.
  • Geographical reasoning: In geographical reasoning, attribution and ablation surface entities such as Kobe Bryant, California, Los Angeles, and the Los Angeles Lakers as intermediate features.
  • Geographical reasoning: 70th, 97th, and 162nd are the activation ranks of the Lakers, California, and Los Angeles area-code features, respectively, while only three of ten strongest activations rank among the ten highest-ablation features.
  • Geographical reasoning: Eight of ten strongest attributed features rank among the ten highest-ablation features, and related prompts distinguish completion-relevant entities from merely subject-related features.

5 Searching for Specific Features

Because the sparse autoencoders contain too many features for exhaustive inspection, the paper develops lightweight search strategies that target concepts, outputs, and feature geometry. Targeted and multi-prompt methods, automated labels, and output attributions help locate interpretable and computationally relevant features.

  • Motivation: The autoencoders contain too many features for exhaustive inspection, motivating methods that search for safety-relevant, abstract, or computationally informative features.
  • Prompt-based search: Targeted prompts identify features that activate strongly for tokens associated with a concept of interest.
  • Prompt-based search: Automated interpretability labels make feature meanings easier to assess at a glance by providing helpful variable-name-like summaries.
  • Prompt-based search: Multi-prompt filtering selects features active across related prompts while negative prompts exclude features responding to confounding content.
  • Image search: Image searches particularly require negative-prompt filtering because content-nonspecific features activate across many image prompts.
  • Image search: For a Golden Gate Bridge image, filtering reveals the bridge feature alongside features for San Francisco places, phone numbers, and landmarks.
  • Geometric search: Feature-vector geometry supports searches through nearest neighbors with high cosine similarity to features of interest.
  • Output-based search: Output attributions rank features by their contribution to a logit difference between candidate completions, proving essential for finding computationally relevant features and refusal-related features.

6 Safety-Relevant Features

The paper identifies safety-relevant features for unsafe code, bias, sycophancy, deception, power seeking, and harmful information. Manipulating several of these features changes model behavior consistently with their interpreted concepts, while their existence alone does not establish model danger.

  • Overview: The study finds features for unsafe code, bias, sycophancy, deception, power seeking, and dangerous or criminal information that causally influence outputs consistently with their interpretations.
  • Safety-relevant code: Three code features represent unsafe code, code errors, and backdoors; manipulating them can induce buffer overflows, mistaken exception beliefs, or backdoor-writing behavior.
  • Safety-relevant code: Unsafe-code and backdoor features also activate on images depicting security bypasses, hidden cameras, hidden recordings, keyloggers, or concealed USB devices.
  • Safety-relevant code: The backdoor feature can cause Claude to write a backdoor that opens a port and sends user input, with comments and names reflecting that behavior.
  • Bias and sycophancy: Clamping more hateful bias-related features to active causes hateful screeds, while another feature shifts responses toward female-pronoun completions and discussion of gender bias in professions.
  • Bias and sycophancy: Sycophancy-related features include empathy and praise, and manipulating them changes model behavior in corresponding ways.
  • Deception and power seeking: Features associated with self-improvement, influence, coups, concealment, and secrecy induce corresponding behaviors when clamped.
  • Harm-related information: Clamping a scam-email feature can make the model write a scam email despite harmlessness training, while a general harm feature is strongly attributed to a refusal completion.

7 Discussion

The discussion highlights promising generalization and causal steering results while emphasizing that safety interpretations and feature coverage remain preliminary. It identifies fundamental challenges in optimizing interpretability, resolving cross-layer and other forms of superposition, and evaluating or scaling feature discovery.

  • Safety interpretation: Safety-relevant investigations remain nascent, so the authors caution against inferring too much from preliminary results.They expect their understanding of these features to evolve rapidly.
  • Promising properties: SAE features generalize zero-shot to images despite being trained only on text activations.The authors describe this cross-distribution behavior as a positive sign.
  • Promising properties: Features often respond to both abstract discussions and concrete examples of the same concept.The security-vulnerability feature responds to abstract discussion and specific vulnerabilities in code.
  • Open problems: Other barriers include shrinkage, attention and weight superposition, limited scientific understanding of superposition, and the sheer number of features and circuits.These challenges require more efficient algorithms and novel research.
  • Evaluation: Reconstruction accuracy and sparsity are only proxies for interpretability, and the appropriate trade-off between them is unclear.Scaling laws can optimize the SAE loss scientifically without necessarily targeting the fundamental interpretability objective.
  • Superposition: Cross-layer superposition can smear features across layers, making features partly represented by later layers difficult to interpret.The authors describe this as a fundamental challenge that current approaches only partially address.
  • Coverage and scale: The feature suite is incomplete, with no estimate of how many Sonnet features exist or whether the discovered set is exhaustive.The authors believe they may be orders of magnitude short, especially across all layers, and note that exhaustive discovery could require prohibitive compute.

8 Related Work

The related-work discussion situates sparse autoencoders within dictionary learning, superposition, disentanglement, compressed sensing, and mechanistic interpretability. It reviews applications, extensions, limitations, and steering approaches that motivate the paper’s unsupervised feature-based perspective.

  • Superposition and dictionary learning: Superposition describes representing more than n features in an n-dimensional neural-network layer.The discussion connects this idea to compressed sensing, mathematical frames, and distributed representations.
  • Superposition and dictionary learning: Dictionary learning models dense activations as sparse linear combinations of unknown feature vectors.In this context, neurons are treated as data to explain and features as sparse factors to infer.
  • Sparse autoencoders: Sparse autoencoders have been shown to extract interpretable, monosemantic features from transformers.Related work also includes binary-feature dictionary learning and applications to Othello-GPT, vision transformers, and attention outputs.
  • Sparse autoencoder extensions: Prior work investigates shrinkage, alternative reconstruction losses, composed or missing features, residual errors, and open-source sparse autoencoders.These studies probe both improvements and limits of the approach.
  • Disentanglement and compressed sensing: The paper distinguishes dictionary learning from disentanglement by focusing on more latent features than observed dimensions.It characterizes dictionary learning as more closely related to compressed sensing.
  • Activation steering: Activation steering modifies model activations during a forward pass to influence downstream behavior.Steering directions can come from probes, dictionary-learning features, embedding arithmetic, or concept-scrubbing methods.
  • Activation steering: This work differs from typical steering by constructing dictionary-learning features unsupervised rather than selecting target behaviors through supervised procedures.The experiments prioritize establishing expected causal effects, not optimizing steering performance, and do not rigorously compare against other steering methods.

A.2 Paper Results

The appendix credits contributors across feature search, interpretability assessment, computational-intermediate experiments, safety-relevant features, visualization, and project leadership. It also identifies the leads responsible for the paper’s major sections.

  • Feature survey: Feature-survey work covered feature completeness, labeling, neighborhoods, UMAPs, clustering, and families such as famous individuals, geography, code, and lists.These activities were distributed across several contributors.
  • Features as computational intermediates: Computational-intermediate experiments used ablation, attribution, visualizations, and scaled tooling to analyze millions of features.The work included emotional-inference and multi-step-inference examples and comparisons among activations, attributions, and ablation effects.
  • Searching for specific features: Feature-search methods included multiple prompts, Claude-generated datasets and prompts, trained classifiers, attribution methods, and nearest-neighbor dictionary vectors.These methods were used to find specific or related features.
  • Safety-relevant features: Safety-relevant features were identified and compared with few-shot probe baselines, followed by steering experiments.The steering examples were contributed by multiple researchers.
  • Leadership: Section leads coordinated scaling dictionary learning, feature interpretability, feature surveys, computational intermediates, feature search, and safety-relevant features.The appendix separately credits overall project leadership and guidance.

D.1 Dataset Examples

The paper uses activating examples to interpret sparse features and feature steering to test their functional relevance. Examples are sampled across activation levels, while steering clamps selected feature activity in reconstructed residual streams.

  • Dataset examples: Feature interpretation uses maximally activating examples plus randomly sampled examples from activation buckets between maximum activation and zero.Because features are sparse, non-activation is treated as the default condition and activation as the case to understand.
  • Dataset examples: Text examples come from The Pile, excluding Books3, and Common Crawl rather than Claude’s internal training dataset.The text-only source data omits the Human/Assistant-formatted data used for Claude’s fine-tuning.
  • Dataset examples: Image examples are hand-curated, primarily from Wikimedia Commons, rather than randomly sampled.This limits how representative the image examples are.
  • Feature steering: Activating examples do not establish causal links to model behavior, so the authors additionally use feature steering.A feature may respond consistently to something without having a model function.
  • Feature steering: Steering decomposes residual activity into SAE reconstruction and error, clamps one reconstructed feature, preserves the error, and runs downstream layers.The manipulation is applied at every input and token position.
  • Feature steering: Interesting steering effects typically require clamping feature activations outside their observed training-dataset range.The paper notes that perturbation magnitudes vary by feature and that extreme clamping can produce nonsensical behavior.

D.2.1 Comparison to few-shot probe-based steering

The paper compares feature steering with few-shot steering vectors, finding that both can work, but feature steering is often uniquely useful in the few-shot setting. Feature-based methods may reveal steering abstractions without requiring concepts to be specified in advance, although rigorous comparisons remain future work.

  • Feature steering and few-shot steering were compared qualitatively using model outputs altered consistently with the interpreted feature.The experiments focused on concepts whose features could be found quickly from one or two positive and negative examples.
  • Few-shot steering vectors were constructed by subtracting mean middle-layer residual-stream activity on negative examples from positive examples.The examples were selected to represent the concept of interest while attempting to control for other confounds.
  • The scaling factor for few-shot steering vectors was manually tuned with a binary-search-like protocol, up to a resolution of 0.1.Too-strong factors produced nonsensical outputs, whereas too-weak factors produced no meaningful change.
  • In seven successful feature-steering examples, few-shot steering vectors were similarly effective in two cases but not five.The five cases included secrecy, sycophancy, code errors, self-improving AI, and developing methamphetamine.
  • The authors expect feature value to come primarily from unsupervised discovery of useful abstractions, while acknowledging that non-feature-based methods may match or exceed features outside the few-shot regime.A rigorous comparison of steering approaches is left to future work.

E Ablations and Attributions

The paper evaluates whether feature activations, attributions, and ablations track one another on selected examples. Attributions correlate much more strongly with ablation effects than activations do, supporting attribution as an efficient proxy for causal feature effects.

  • The analysis comprehensively evaluates relationships among feature activations, attributions, and ablation effects.The evaluation uses selected examples from the Features as Computational Intermediates section.
  • About .81 was the correlation between attributions and ablations, compared with .12 between activations and ablations.These measurements were made on the “John” and first “Kobe” examples.
  • The authors interpret the attribution–ablation relationship as confirming that attribution can efficiently approximate the gold-standard causal effect of feature ablations.They note that AtP* could provide a better approximation by adjusting for attention-pattern saturation.

F More safety-relevant features

The paper presents a broad collection of safety-relevant features, spanning deception, social manipulation, bias, harmful content, dangerous behavior, and catastrophic AI risks. The examples include both concrete topics and abstract or socially meaningful concepts, with descriptions generated mostly by Claude and sometimes written manually.

  • Bias and social attitudes: The collection includes political, gender, racial, religious, and cultural bias-related features.Examples cover gender stereotypes, women’s capabilities, political criticism, support for Donald Trump, American exceptionalism, and associations between Muslims and terrorism.
  • Toxicity, hate, and abuse: Safety-relevant features represent harmful and abusive content, including hate speech, racist claims, violence, profanity, slurs, and exploitation of minors.The listed features include both broad abuse categories and specific forms of harm.
  • Dangers of artificial intelligence: Features cover catastrophic and existential AI-risk concepts, including uncontrollable advanced AI, instrumental goals, sentience, self-improvement, and harm to humans.Additional examples concern optimization, agency, coherence, AI-generated languages, and scenarios described as catastrophic or existential risks.
  • Dangerous or criminal behavior: The collection includes dangerous or criminal behavior involving drugs, scams, weapons, explosives, terrorism, and hazardous chemical or biological agents.Examples range from illegal drug manufacture and scam emails to weapons acquisition, bomb-making, bioweapons, and engineered viruses.
  • Deception and social manipulation: The feature collection includes deception and social manipulation, such as fooling others, misleading institutions, ulterior motives, and concealed danger.Examples include people pretending to act or lying about actions, and revealing true goals after a triggering event.
  • Additional feature examples: The feature set also contains ordinary concepts and phenomena, including hidden meanings, ghosts, code for language-model prompts, and limitations of large language models.These examples illustrate the breadth of the listed feature descriptions beyond explicitly harmful topics.
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