mlsanityData to 14 Oct 2026

SAIF: A Sparse Autoencoder Framework for Interpreting and Steering Instruction Following of Language Models

arXiv 2502.11356 ↗First posted 17 Feb 2025cs.LG cs.AI cs.CLcited by 34

On the map: Mechanistic interpretability → SAE features and their applications

Abstract (arXiv)

The ability of large language models (LLMs) to follow instructions is crucial for their practical applications, yet the underlying mechanisms remain poorly understood. This paper presents a novel framework that leverages sparse autoencoders (SAE) to interpret how instruction following works in these models. We demonstrate how the features we identify can effectively steer model outputs to align with given instructions. Through analysis of SAE latent activations, we identify specific latents responsible for instruction following behavior. Our findings reveal that instruction following capabilities are encoded by a distinct set of instruction-relevant SAE latents. These latents both show semantic proximity to relevant instructions and demonstrate causal effects on model behavior. Our research highlights several crucial factors for achieving effective steering performance: precise feature identification, the role of final layer, and optimal instruction positioning. Additionally, we demonstrate that our methodology scales effectively across SAEs and LLMs of varying sizes.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). Placed in SAE features and their applications, one of 13 directions in Mechanistic interpretability. Builds on 21 of its 27 references that are in the map. First posted on arXiv 17 Feb 2025.

Builds on

21 of 27 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Llama Scope: Extracting Millions of Features from Llama-3.1-8B with Sparse Autoencoders27 Oct
  2. 2024Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders19 Jul

Other references · 19

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  3. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,470
  4. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,336
  5. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,319
  6. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,302
  7. 2023LIMA: Less Is More for Alignmentcited by 1,100
  8. 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,085
  9. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 1,032
  10. 2018XNLI: Evaluating Cross-lingual Sentence Representationscited by 915
  11. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 876
  12. 2023The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasetscited by 712
7 more
  1. 2024Improving Instruction-Following in Language Models through Activation Steeringcited by 160
  2. 2024Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Modelscited by 115
  3. 2023Evaluating Large Language Models on Controlled Generation Taskscited by 73
  4. 2023Improving Activation Steering in Language Models with Mean-Centringcited by 68
  5. 2023Do Models Really Learn to Follow Instructions? An Empirical Study of Instruction Tuningcited by 49
  6. 2024Beyond Single Concept Vector: Modeling Concept Subspace in LLMs with Gaussian Distributioncited by 27
  7. 2023(Why) Is My Prompt Getting Worse? Rethinking Regression Testing for Evolving LLM APIscited by 23

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 14; 2026: 20.

34 papers.

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  1. 2025A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Modelscited by 76
  2. 2025SAE-SSV: Supervised Steering in Sparse Representation Spaces for Reliable Control of Language Modelscited by 29
  3. 2026Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Modelscited by 27
  4. 2026Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Modelscited by 21
  5. 2025Feature Extraction and Steering for Enhanced Chain-of-Thought Reasoning in Language Modelscited by 13
  6. 2026Steering Vector Fields for Context-Aware Inference-Time Control in Large Language Modelscited by 13
  7. 2026Do Sparse Autoencoders Identify Reasoning Features in Language Models?cited by 12
  8. 2025Enhancing LLM Steering through Sparse Autoencoder-Based Vector Refinementcited by 10
  9. 2025Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steeringcited by 9
  10. 2025Turning the Spell Around: Lightweight Alignment Amplification via Rank-One Safety Injectioncited by 5
  11. 2025Beyond Input Activations: Identifying Influential Latents by Gradient Sparse Autoencoderscited by 4
  12. 2025Sparse Activation Editing for Reliable Instruction Following in Narrativescited by 4
22 more
  1. 2025Evaluating Sparse Autoencoders for Monosemantic Representationcited by 4
  2. 2026SegCompass: Exploring Interpretable Alignment with Sparse Autoencoders for Enhanced Reasoning Segmentationcited by 4
  3. 2025Bottlenecked Transformers: Periodic KV Cache Consolidation for Generalised Reasoningcited by 3
  4. 2025AHAMask: Reliable Task Specification for Large Audio Language Models without Instructionscited by 3
  5. 2026NeuronScope: A Multi-Agent Framework for Explaining Polysemantic Neurons in Language Modelscited by 3
  6. 2026Steering LLMs? Actually, Sparse Autoencoders can outperform simple baselinescited by 3
  7. 2026Where Steering Signals Come From: Activation Source Selection in Activation Steeringcited by 3
  8. 2025SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selectioncited by 2
  9. 2025Evaluating Steering Techniques using Human Similarity Judgmentscited by 2
  10. 2026A Layer-wise Analysis of Supervised Fine-Tuningcited by 2
  11. 2025Breaking the Block: Preserving Data Continuity to Train Superior SAEs for Instruct Modelscited by 1
  12. 2026Patches of Nonlinearity: Instruction Vectors in Large Language Modelscited by 1
  13. 2026High-Dimensional Random Projection for Activation Steering in Language Modelscited by 1
  14. 2026Enhancing SAE-based Steering via Neighbor Integrated Feature Selectioncited by 1
  15. 2026How LLMs Follow Instructions: Skillful Coordination, Not a Universal Mechanismcited by 0
  16. 2026Steered Generation via Gradient-Based Optimization on Sparse Query Featurescited by 0
  17. 2026Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Modelscited by 0
  18. 2026Refusal geometry reflects refusal training: diverse refusal prefixes can raise stable rank and weaken refusal vector ablation attackscited by 0
  19. 2026S^3martCirc: Self-supervised Smart Circuit Discoverycited by 0
  20. 2026Beyond Token Scale: Chunk-Level Sparse Autoencoders for Reliable Semantic Feature Discoverycited by 0
  21. 2026MetaSteer: Context-Conditioned, nonlinear Steering via Attention-Projection Adaptationcited by 0
  22. 2026Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utilitycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities
  2. 2025A Comparative Analysis of Sparse Autoencoder and Activation Difference in Language Model Steering
  3. 2025Steering Large Language Model Activations in Sparse Spaces
  4. 2024SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models
  5. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructions
  6. 2025Aligning Instruction Tuning with Pre-training
  7. 2023FollowEval: A Multi-Dimensional Benchmark for Assessing the Instruction-Following Capability of Large Language Models
  8. 2025Instruction Tuning with and without Context: Behavioral Shifts and Downstream Impact