mlsanityData to 13 Oct 2026

On the Limitations of Steering in Language Model Alignment

arXiv 2505.01162 ↗First posted 2 May 2025cs.CL cs.AIcited by 3

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

also in 1 more
Abstract (arXiv)

Steering vectors are a promising approach to aligning language model behavior at inference time. In this paper, we propose a framework to assess the limitations of steering vectors as alignment mechanisms. Using a framework of transformer hook interventions and antonym-based function vectors, we evaluate the role of prompt structure and context complexity in steering effectiveness. Our findings indicate that steering vectors are promising for specific alignment tasks, such as value alignment, but may not provide a robust foundation for general-purpose alignment in LLMs, particularly in complex scenarios. We establish a methodological foundation for future investigations into steering capabilities of reasoning models.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 10 of its 12 references that are in the map. First posted on arXiv 2 May 2025.

Builds on

10 of 12 references.

References · 10

  1. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,986
  2. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,280
  3. 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,042
  4. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 1,009
  5. 2023Steering Language Models With Activation Engineeringcited by 873
  6. 2023Bias and Fairness in Large Language Models: A Surveycited by 636
  7. 2023Linear Representations of Sentiment in Large Language Modelscited by 161
  8. 2024Analyzing the Generalization and Reliability of Steering Vectorscited by 138
  9. 2023Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimismcited by 69
  10. 2024Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalabilitycited by 40

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Mitigating the Safety Alignment Tax with Null-Space Constrained Policy Optimizationcited by 11
  2. 2025Polarity-Aware Probing for Quantifying Latent Alignment in Language Modelscited by 1
  3. 2026Mechanistic Indicators of Steering Effectiveness in Large Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Where to Steer: Input-Dependent Layer Selection for Steering Improves LLM Alignment
  2. 2025Understanding (Un)Reliability of Steering Vectors in Language Models
  3. 2026Steering Vector Fields for Context-Aware Inference-Time Control in Large Language Models
  4. 2026ODESteer: A Unified ODE-Based Steering Framework for LLM Alignment
  5. 2026Probabilistic Concept-Aware Steering for Trustworthy LLM Inference
  6. 2025Self-Improving Model Steering
  7. 2026Steer2Adapt: Dynamically Composing Steering Vectors Elicits Efficient Adaptation of LLMs
  8. 2026What Drives Representation Steering? A Mechanistic Case Study on Steering Refusal