# LogitLens4LLMs: Extending Logit Lens Analysis to Modern Large Language Models

arXiv: https://arxiv.org/abs/2503.11667 · HTML: https://mlsanity.com/paper/2503.11667/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge and representations inside LLMs, one of 13 directions in Mechanistic interpretability.
- Builds on 8 of its 10 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Placed in

- Mechanistic interpretability / [Knowledge and representations inside LLMs](https://mlsanity.com/mechinterp/knowledge-and-representations-inside-llms/)

## Builds on (most cited first, 8 of 8)

- [Eliciting Latent Predictions from Transformers with the Tuned Lens](https://mlsanity.com/paper/2303.08112/) (2023)
- [Do Llamas Work in English? On the Latent Language of Multilingual Transformers](https://mlsanity.com/paper/2402.10588/) (2024)
- [Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models](https://mlsanity.com/paper/2401.06102/) (2024)
- [The Semantic Hub Hypothesis: Language Models Share Semantic Representations Across Languages and Modalities](https://mlsanity.com/paper/2411.04986/) (2024)
- [Understanding and Patching Compositional Reasoning in LLMs](https://mlsanity.com/paper/2402.14328/) (2024)
- [Backward Lens: Projecting Language Model Gradients into the Vocabulary Space](https://mlsanity.com/paper/2402.12865/) (2024)
- [Mechanistic interpretability of large language models with applications to the financial services industry](https://mlsanity.com/paper/2407.11215/) (2024)
- [Unraveling Token Prediction Refinement and Identifying Essential Layers in Language Models](https://mlsanity.com/paper/2501.15054/) (2025)

## Built on by (most cited first, 23 of 23)

- [Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models](https://mlsanity.com/paper/2601.14004/) (2026)
- [AudioLens: A Closer Look at Auditory Attribute Perception of Large Audio-Language Models](https://mlsanity.com/paper/2506.05140/) (2025)
- [From Data to Behavior: Predicting Unintended Model Behaviors Before Training](https://mlsanity.com/paper/2602.04735/) (2026)
- [Rethinking Explainability in the Era of Multimodal AI](https://mlsanity.com/paper/2506.13060/) (2025)
- [Global Evolutionary Steering: Refining Activation Steering Control via Cross-Layer Consistency](https://mlsanity.com/paper/2603.12298/) (2026)
- [SafeSeek: Universal Attribution of Safety Circuits in Language Models](https://mlsanity.com/paper/2603.23268/) (2026)
- [Distributional Semantics Tracing: A Framework for Explaining Hallucinations in Large Language Models](https://mlsanity.com/paper/2510.06107/) (2025)
- [Layer-Order Inversion: Rethinking Latent Multi-Hop Reasoning in Large Language Models](https://mlsanity.com/paper/2601.03542/) (2026)
- [DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Search](https://mlsanity.com/paper/2508.20353/) (2025)
- [When Does a Language Model Commit? A Finite-Answer Theory of Pre-Verbalization Commitment](https://mlsanity.com/paper/2605.06723/) (2026)
- [Understanding Performance Collapse in Layer-Pruned Large Language Models via Decision Representation Transitions](https://mlsanity.com/paper/2605.07271/) (2026)
- [When Reasoning Traces Become Performative: Step-Level Evidence that Chain-of-Thought Is an Imperfect Oversight Channel](https://mlsanity.com/paper/2605.11746/) (2026)
- [Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of Intelligence](https://mlsanity.com/paper/2608.12036/) (2026)
- [On the Effect of Uncertainty on Layer-wise Inference Dynamics](https://mlsanity.com/paper/2507.06722/) (2025)
- [Momentum Point-Perplexity Mechanics in Large Language Models](https://mlsanity.com/paper/2508.08492/) (2025)
- [LoRA-Drop: Temporal LoRA Decoding for Efficient LLM Inference](https://mlsanity.com/paper/2601.02569/) (2026)
- [Sparse Readout Prism: Explaining Logit-Lens Scores in Features Instead of Tokens](https://mlsanity.com/paper/2609.01936/) (2026)
- [Mechanistic Indicators of Steering Effectiveness in Large Language Models](https://mlsanity.com/paper/2602.01716/) (2026)
- [HyperLens: Quantifying Cognitive Effort in LLMs with Fine-grained Confidence Trajectory](https://mlsanity.com/paper/2605.05741/) (2026)
- [Arithmetic Pedagogy for Language Models](https://mlsanity.com/paper/2606.05106/) (2026)
- [MechLens: Late Crystallization of Factual Knowledge Explains Intervention Effectiveness in Language Models](https://mlsanity.com/paper/2606.07978/) (2026)
- [When Can We Trust the Sparse Lens? A Certification Framework for SAE Faithfulness](https://mlsanity.com/paper/2606.18383/) (2026)
- [VisLens: Single-Pass Interpretable Visual Search for Multimodal LLMs](https://mlsanity.com/paper/2608.30705/) (2026)

## Abstract (arXiv)

This paper introduces LogitLens4LLMs, a toolkit that extends the Logit Lens technique to modern large language models. While Logit Lens has been a crucial method for understanding internal representations of language models, it was previously limited to earlier model architectures. Our work overcomes the limitations of existing implementations, enabling the technique to be applied to state-of-the-art architectures (such as Qwen-2.5 and Llama-3.1) while automating key analytical workflows. By developing component-specific hooks to capture both attention mechanisms and MLP outputs, our implementation achieves full compatibility with the HuggingFace transformer library while maintaining low inference overhead. The toolkit provides both interactive exploration and batch processing capabilities, supporting large-scale layer-wise analyses. Through open-sourcing our implementation, we aim to facilitate deeper investigations into the internal mechanisms of large-scale language models. The toolkit is openly available at https://github.com/zhenyu-02/LogitLens4LLMs.