Cover Image

Constructing Interpretable Features from Compositional Neuron Groups

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shafran, Or, Geiger, Atticus, Geva, Mor
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations
by: Huang, Jing, et al.
Published: (2024)

From Directions to Regions: Decomposing Activations in Language Models via Local Geometry
by: Shafran, Or, et al.
Published: (2026)

Enhancing Automated Interpretability with Output-Centric Feature Descriptions
by: Gur-Arieh, Yoav, et al.
Published: (2025)

Mixing Mechanisms: How Language Models Retrieve Bound Entities In-Context
by: Gur-Arieh, Yoav, et al.
Published: (2025)

Routers Learn the Geometry of Their Experts: Geometric Coupling in Sparse Mixture-of-Experts
by: Ahrac, Sagi, et al.
Published: (2026)

How Causal Abstraction Underpins Computational Explanation
by: Geiger, Atticus, et al.
Published: (2025)

How Do Transformers Learn Variable Binding in Symbolic Programs?
by: Wu, Yiwei, et al.
Published: (2025)

ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning
by: She, Jingyuan Selena, et al.
Published: (2023)

HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
by: Sun, Jiuding, et al.
Published: (2025)

A Reply to Makelov et al. (2023)'s "Interpretability Illusion" Arguments
by: Wu, Zhengxuan, et al.
Published: (2024)

Backward Lens: Projecting Language Model Gradients into the Vocabulary Space
by: Katz, Shahar, et al.
Published: (2024)

Activation Steering via Generative Causal Mediation
by: Sankaranarayanan, Aruna, et al.
Published: (2026)

Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models
by: Ghandeharioun, Asma, et al.
Published: (2024)

Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions
by: Parmar, Mihir, et al.
Published: (2022)

Disentangling MLP Neuron Weights in Vocabulary Space
by: Avrahamy, Asaf, et al.
Published: (2026)

HyperSteer: Activation Steering at Scale with Hypernetworks
by: Sun, Jiuding, et al.
Published: (2025)

Mechanistically Interpretable Neural Encoding Reveals Fine-Grained Functional Selectivity in Human Visual Cortex
by: Grosbard, Idan Daniel, et al.
Published: (2026)

Evaluating Open-Source Sparse Autoencoders on Disentangling Factual Knowledge in GPT-2 Small
by: Chaudhary, Maheep, et al.
Published: (2024)

pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
by: Wu, Zhengxuan, et al.
Published: (2024)

Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought
by: Boppana, Siddharth, et al.
Published: (2026)

Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents
by: Shafran, Avital, et al.
Published: (2024)

When Can Transformers Count to n?
by: Yehudai, Gilad, et al.
Published: (2024)

ReFT: Representation Finetuning for Language Models
by: Wu, Zhengxuan, et al.
Published: (2024)

Improving Neuron-level Interpretability with White-box Language Models
by: Bai, Hao, et al.
Published: (2024)

Inferring Functionality of Attention Heads from their Parameters
by: Elhelo, Amit, et al.
Published: (2024)

Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space
by: Bigelow, Eric, et al.
Published: (2026)

Estimating Knowledge in Large Language Models Without Generating a Single Token
by: Gottesman, Daniela, et al.
Published: (2024)

Transcoders Find Interpretable LLM Feature Circuits
by: Dunefsky, Jacob, et al.
Published: (2024)

MIB: A Mechanistic Interpretability Benchmark
by: Mueller, Aaron, et al.
Published: (2025)

Simple Mechanistic Explanations for Out-Of-Context Reasoning
by: Wang, Atticus, et al.
Published: (2025)

Automatically Interpreting Millions of Features in Large Language Models
by: Paulo, Gonçalo, et al.
Published: (2024)

Analyze Feature Flow to Enhance Interpretation and Steering in Language Models
by: Laptev, Daniil, et al.
Published: (2025)

Towards Interpreting Visual Information Processing in Vision-Language Models
by: Neo, Clement, et al.
Published: (2024)

Interpretability at Scale: Identifying Causal Mechanisms in Alpaca
by: Wu, Zhengxuan, et al.
Published: (2023)

Eliciting Textual Descriptions from Representations of Continuous Prompts
by: Ramati, Dana, et al.
Published: (2024)

NeuronScope: A Multi-Agent Framework for Explaining Polysemantic Neurons in Language Models
by: Liu, Weiqi, et al.
Published: (2026)

AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders
by: Wu, Zhengxuan, et al.
Published: (2025)

Language Models Encode Numbers Using Digit Representations in Base 10
by: Levy, Amit Arnold, et al.
Published: (2024)

PrivacyScalpel: Enhancing LLM Privacy via Interpretable Feature Intervention with Sparse Autoencoders
by: Frikha, Ahmed, et al.
Published: (2025)

Understanding Gated Neurons in Transformers from Their Input-Output Functionality
by: Gerstner, Sebastian, et al.
Published: (2025)