Awesome LLM Interpretability

A curated list of amazingly awesome tools, papers, articles, and communities focused on Large Language Model (LLM) Interpretability.

Table of Contents

• Awesome LLM Interpretability
  • LLM Interpretability Tools
  • LLM Interpretability Papers
  • LLM Interpretability Articles
  • LLM Interpretability Groups

LLM Interpretability Tools

Tools and libraries for LLM interpretability and analysis.

1. Comgra - Comgra helps you analyze and debug neural networks in pytorch.
2. Pythia - Interpretability analysis to understand how knowledge develops and evolves during training in autoregressive transformers.
3. Phoenix - AI Observability & Evaluation - Evaluate, troubleshoot, and fine tune your LLM, CV, and NLP models in a notebook.
4. Automated Interpretability - Code for automatically generating, simulating, and scoring explanations of neuron behavior.
5. Fmr.ai - AI interpretability and explainability platform.
6. Attention Analysis - Analyzing attention maps from BERT transformer.
7. SpellGPT - Explores GPT-3’s ability to spell own token strings.
8. SuperICL - Super In-Context Learning code which allows black-box LLMs to work with locally fine-tuned smaller models.
9. Git Re-Basin - Code release for "Git Re-Basin: Merging Models modulo Permutation Symmetries.”
10. Functionary - Chat language model that can interpret and execute functions/plugins.
11. Sparse Autoencoder - Sparse Autoencoder for Mechanistic Interpretability.
12. Rome - Locating and editing factual associations in GPT.
13. Inseq - Interpretability for sequence generation models.
14. Neuron Viewer - Tool for viewing neuron activations and explanations.
15. LLM Visualization - Visualizing LLMs in low level.
16. Vanna - Abstractions to use RAG to generate SQL with any LLM

LLM Interpretability Papers

Academic and industry papers on LLM interpretability.

1. Finding Neurons in a Haystack: Case Studies with Sparse Probing - Explores the representation of high-level human-interpretable features within neuron activations of large language models (LLMs).
2. Copy Suppression: Comprehensively Understanding an Attention Head - Investigates a specific attention head in GPT-2 Small, revealing its primary role in copy suppression.
3. Linear Representations of Sentiment in Large Language Models - Shows how sentiment is represented in Large Language Models (LLMs), finding that sentiment is linearly represented in these models.
4. Emergent world representations: Exploring a sequence model trained on a synthetic task - Explores emergent internal representations in a GPT variant trained to predict legal moves in the board game Othello.
5. Towards Automated Circuit Discovery for Mechanistic Interpretability - Introduces the Automatic Circuit Discovery (ACDC) algorithm for identifying important units in neural networks.
6. A Toy Model of Universality: Reverse Engineering How Networks Learn Group Operations - Examines small neural networks to understand how they learn group compositions, using representation theory.
7. Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias - Causal mediation analysis as a method for interpreting neural models in natural language processing.
8. The Quantization Model of Neural Scaling - Proposes the Quantization Model for explaining neural scaling laws in neural networks.
9. Discovering Latent Knowledge in Language Models Without Supervision - Presents a method for extracting accurate answers to yes-no questions from language models' internal activations without supervision.
10. How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model - Analyzes mathematical capabilities of GPT-2 Small, focusing on its ability to perform the 'greater-than' operation.
11. Towards Monosemanticity: Decomposing Language Models With Dictionary Learning - Using a sparse autoencoder to decompose the activations of a one-layer transformer into interpretable, monosemantic features.
12. Language models can explain neurons in language models - Explores how language models like GPT-4 can be used to explain the functioning of neurons within similar models.
13. Emergent Linear Representations in World Models of Self-Supervised Sequence Models - Linear representations in a world model of Othello-playing sequence models.
14. "Toward a Mechanistic Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model" - Explores stepwise inference in autoregressive language models using a synthetic task based on navigating directed acyclic graphs.
15. "Successor Heads: Recurring, Interpretable Attention Heads In The Wild" - Introduces 'successor heads,' attention heads that increment tokens with a natural ordering, such as numbers and days, in LLM’s.
16. "Large Language Models Are Not Robust Multiple Choice Selectors" - Analyzes the bias and robustness of LLMs in multiple-choice questions, revealing their vulnerability to option position changes due to inherent "selection bias”.
17. "Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory" - Presents a novel approach to understanding neural networks by examining feature complexity through category theory.
18. "Let's Verify Step by Step" - Focuses on improving the reliability of LLMs in multi-step reasoning tasks using step-level human feedback.
19. "Interpretability Illusions in the Generalization of Simplified Models" - Examines the limitations of simplified representations (like SVD) used to interpret deep learning systems, especially in out-of-distribution scenarios.
20. "The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Language Models" - Presents a novel approach for identifying and mitigating social biases in language models, introducing the concept of 'Social Bias Neurons'.
21. "Interpreting the Inner Mechanisms of Large Language Models in Mathematical Addition" - Investigates how LLMs perform the task of mathematical addition.
22. "Measuring Feature Sparsity in Language Models" - Develops metrics to evaluate the success of sparse coding techniques in language model activations.
23. Toy Models of Superposition - Investigates how models represent more features than dimensions, especially when features are sparse.
24. Spine: Sparse interpretable neural embeddings - Presents SPINE, a method transforming dense word embeddings into sparse, interpretable ones using denoising autoencoders.
25. Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors - Introduces a novel method for visualizing transformer networks using dictionary learning.
26. Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling - Introduces Pythia, a toolset designed for analyzing the training and scaling behaviors of LLMs.
27. On Interpretability and Feature Representations: An Analysis of the Sentiment Neuron - Critically examines the effectiveness of the "Sentiment Neuron”.
28. Engineering monosemanticity in toy models - Explores engineering monosemanticity in neural networks, where individual neurons correspond to distinct features.
29. Polysemanticity and capacity in neural networks - Investigates polysemanticity in neural networks, where individual neurons represent multiple features.
30. An Overview of Early Vision in InceptionV1 - A comprehensive exploration of the initial five layers of the InceptionV1 neural network, focusing on early vision.
31. Visualizing and measuring the geometry of BERT - Delves into BERT's internal representation of linguistic information, focusing on both syntactic and semantic aspects.
32. Neurons in Large Language Models: Dead, N-gram, Positional - An analysis of neurons in large language models, focusing on the OPT family.
33. Can Large Language Models Explain Themselves? - Evaluates the effectiveness of self-explanations generated by LLMs in sentiment analysis tasks.
34. Interpretability in the Wild: GPT-2 small (arXiv) - Provides a mechanistic explanation of how GPT-2 small performs indirect object identification (IOI) in natural language processing.
35. Sparse Autoencoders Find Highly Interpretable Features in Language Models - Explores the use of sparse autoencoders to extract more interpretable and less polysemantic features from LLMs.
36. Emergent and Predictable Memorization in Large Language Models - Investigates the use of sparse autoencoders for enhancing the interpretability of features in LLMs.
37. Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars - Demonstrates that focusing only on specific parts like attention heads or weight matrices in Transformers can lead to misleading interpretability claims.
38. The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets - This paper investigates the representation of truth in Large Language Models (LLMs) using true/false datasets.
39. Interpretability at Scale: Identifying Causal Mechanisms in Alpaca - This study presents Boundless Distributed Alignment Search (Boundless DAS), an advanced method for interpreting LLMs like Alpaca.
40. Representation Engineering: A Top-Down Approach to AI Transparency - Introduces Representation Engineering (RepE), a novel approach for enhancing AI transparency, focusing on high-level representations rather than neurons or circuits.
41. Explaining black box text modules in natural language with language models - Natural language explanations for LLM attention heads, evaluated using synthetic text
42. N2G: A Scalable Approach for Quantifying Interpretable Neuron Representations in Large Language Models - Explain each LLM neuron as a graph
43. Augmenting Interpretable Models with LLMs during Training - Use LLMs to build interpretable classifiers of text data

LLM Interpretability Articles

Insightful articles and blog posts on LLM interpretability.

1. A New Approach to Computation Reimagines Artificial Intelligenceg - Discusses hyperdimensional computing, a novel method involving hyperdimensional vectors (hypervectors) for more efficient, transparent, and robust artificial intelligence.
2. Interpreting GPT: the logit lens - Explores how the logit lens, reveals a gradual convergence of GPT's probabilistic predictions across its layers, from initial nonsensical or shallow guesses to more refined predictions.
3. A Mechanistic Interpretability Analysis of Grokking - Explores the phenomenon of 'grokking' in deep learning, where models suddenly shift from memorization to generalization during training.
4. 200 Concrete Open Problems in Mechanistic Interpretability - Series of posts discussing open research problems in the field of Mechanistic Interpretability (MI), which focuses on reverse-engineering neural networks.
5. Evaluating LLMs is a minefield - Challenges in assessing the performance and biases of large language models (LLMs) like GPT.
6. Attribution Patching: Activation Patching At Industrial Scale - Method that uses gradients for a linear approximation of activation patching in neural networks.
7. Causal Scrubbing: a method for rigorously testing interpretability hypotheses [Redwood Research] - Introduces causal scrubbing, a method for evaluating the quality of mechanistic interpretations in neural networks.
8. A circuit for Python docstrings in a 4-layer attention-only transformer - Proposes the Quantization Model for explaining neural scaling laws in neural networks.
9. Discovering Latent Knowledge in Language Models Without Supervision - Examines a specific neural circuit within a 4-layer transformer model responsible for generating Python docstrings.
10. Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks - Survey on mechanistic interpretability

LLM Interpretability Groups

Communities and groups dedicated to LLM interpretability.

1. Alignment Lab AI - Group of researchers focusing on AI alignment.
2. Nous Research - Research group discussing various topics on interpretability.
3. EleutherAI - Non-profit AI research lab that focuses on interpretability and alignment of large models.

Contributing and Collaborating

Please see CONTRIBUTING and CODE-OF-CONDUCT for details.