A Primer on the Inner Workings of Transformer-based Language Models
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Cited by 23 in Mechanistic interpretability, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
The rapid progress of research aimed at interpreting the inner workings of advanced language models has highlighted a need for contextualizing the insights gained from years of work in this area. This primer provides a concise technical introduction to the current techniques used to interpret the inner workings of Transformer-based language models, focusing on the generative decoder-only architecture. We conclude by presenting a comprehensive overview of the known internal mechanisms implemented by these models, uncovering connections across popular approaches and active research directions in this area.
Key facts. Cited by 85 papers in the map (data through 14 Oct 2026). Builds on 198 of its 410 references that are in the map. First posted on arXiv 30 Apr 2024.
Builds on
198 of 410 references.
References · 198
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- 2022In-context Learning and Induction Headscited by 964
- 2019Attention is not Explanationcited by 929
- 2023Steering Language Models With Activation Engineeringcited by 922
- 2024Gemma: Open Models Based on Gemini Research and Technologycited by 916
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- 2023Progress measures for grokking via mechanistic interpretabilitycited by 839
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50 most cited of 198 All 198 (JSON)
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Citing papers per year: 2024: 28; 2025: 29; 2026: 28.
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- 2024Transcoders Find Interpretable LLM Feature Circuitscited by 200
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- 2024Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Modelscited by 115
- 2024Knowledge Circuits in Pretrained Transformerscited by 60
- 2025MIB: A Mechanistic Interpretability Benchmarkcited by 56
- 2024Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Modelscited by 51
- 2024Residual Stream Analysis with Multi-Layer SAEscited by 19
- 2024Anchored Answers: Unravelling Positional Bias in GPT-2's Multiple-Choice Questionscited by 15
- 2025Signal in the Noise: Polysemantic Interference Transfers and Predicts Cross-Model Influencecited by 4
- 2026Putting a Face to Forgetting: Continual Learning meets Mechanistic Interpretabilitycited by 4
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12 most cited of 18 All 18 (JSON)
- 2026The Communication Map of a Transformer22 Aug 2026
- 2026Individual Parameters in Weight-Sparse Transformers Appear Interpretable3 Jul 2026
- 2026Data-driven Circuit Discovery for Interpretability of Language Models9 May 2026
- 2026Contrastive Attribution in the Wild: An Interpretability Analysis of LLM Failures on Realistic Benchmarks20 Apr 2026
- 2026Language Models Can Explain Visual Features via Steering23 Mar 2026
- 2026Putting a Face to Forgetting: Continual Learning meets Mechanistic Interpretability29 Jan 2026
- 2025DePass: Unified Feature Attributing by Simple Decomposed Forward Pass21 Oct 2025
- 2025Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations20 Oct 2025
- 2025Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures29 Sep 2025
- 2025Signal in the Noise: Polysemantic Interference Transfers and Predicts Cross-Model Influence16 May 2025
- 2025MIB: A Mechanistic Interpretability Benchmark17 Apr 2025
- 2024Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models21 Nov 2024
12 newest of 18 All 18 (JSON)
- 2024Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalizationcited by 79
- 2025Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspectivecited by 34
- 2024Calibrating Reasoning in Language Models with Internal Consistencycited by 28
- 2025The Geometry of Reasoning: Flowing Logics in Representation Spacecited by 21
- 2025Are language models aware of the road not taken? Token-level uncertainty and hidden state dynamicscited by 11
- 2026Rethinking Layer Relevance in Large Language Models Beyond Cosine Similaritycited by 4
- 2026Step-Level Sparse Autoencoder for Reasoning Process Interpretationcited by 3
- 2025Deductive Logic in Language Models: Horizontal vs Vertical Reasoningcited by 1
- 2026CircuitProbe: Predicting Reasoning Circuits in Transformers via Stability Zone Detectioncited by 1
- 2025InTraVisTo: Inside Transformer Visualisation Toolcited by 0
- 2026Rethinking Layer Relevance in Large Language Models Beyond Cosine Similarity13 May 2026
- 2026CircuitProbe: Predicting Reasoning Circuits in Transformers via Stability Zone Detection1 Apr 2026
- 2026Step-Level Sparse Autoencoder for Reasoning Process Interpretation3 Mar 2026
- 2025Are language models aware of the road not taken? Token-level uncertainty and hidden state dynamics6 Nov 2025
- 2025The Geometry of Reasoning: Flowing Logics in Representation Space10 Oct 2025
- 2025Deductive Logic in Language Models: Horizontal vs Vertical Reasoning10 Oct 2025
- 2025InTraVisTo: Inside Transformer Visualisation Tool18 Jul 2025
- 2025Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspective19 May 2025
- 2024Calibrating Reasoning in Language Models with Internal Consistency29 May 2024
- 2024Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization23 May 2024
- 2024The Remarkable Robustness of LLMs: Stages of Inference?cited by 146
- 2024LLM Circuit Analyses Are Consistent Across Training and Scalecited by 63
- 2024Confidence Regulation Neurons in Language Modelscited by 57
- 2024Emergence of a High-Dimensional Abstraction Phase in Language Transformerscited by 56
- 2024NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internalscited by 45
- 2025Overcoming Sparsity Artifacts in Crosscoders to Interpret Chat-Tuningcited by 34
- 2024Out-of-distribution generalization via composition: a lens through induction heads in Transformerscited by 28
- 2024Can Knowledge Editing Really Correct Hallucinations?cited by 28
- 2024Beyond Single Concept Vector: Modeling Concept Subspace in LLMs with Gaussian Distributioncited by 27
- 2025Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysiscited by 20
- 2025Correctness Assessment of Code Generated by Large Language Models Using Internal Representationscited by 17
- 2025Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuningcited by 16
12 most cited of 44 All 44 (JSON)
- 2026On the Necessity of Attention-FFN Split in Vision Transformers7 Oct 2026
- 2026Virgil: Navigating Explainability for Transformer-based Language Models26 Aug 2026
- 2026Mechanistic Circuit Identification for Controllable Data Generation25 Aug 2026
- 2026On the use of foundation models in cognitive science7 Aug 2026
- 2026Weight-Adjusted Gradients Reveal Parameter Importance and Failure Modes in LLMs12 Jul 2026
- 2026Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning1 Jun 2026
- 2026Contribution Weights: A Geometrical Analysis of Self-Attention Transformers29 May 2026
- 2026Relational Linear Properties in Language Models: An Empirical Investigation21 May 2026
- 2026Enabling Performant and Flexible Model-Internal Observability for LLM Inference11 May 2026
- 2026Exploring Language-Agnosticity in Function Vectors: A Case Study in Machine Translation21 Apr 2026
- 2026ConceptTracer: Interactive Analysis of Concept Saliency and Selectivity in Neural Representations8 Apr 2026
- 2026In-Context Learning in Speech Language Models: Analyzing the Role of Acoustic Features, Linguistic Structure, and Induction Heads7 Apr 2026
12 newest of 44 All 44 (JSON)