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The Answer-Basin Representation Hypothesis: We Are Not Probing or Steering Concepts

arXiv 2609.24821 ↗First posted 21 Sep 2026cs.CLcited by 0

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Abstract (arXiv)

Linear probing and activation steering use linear directions to predict and control behavior-level concepts such as correctness, safety, and social bias in question answering. We call these behavior-level concept directions. Drawing on the Linear Representation Hypothesis (LRH) and circuit studies, these directions are often interpreted as concept representations. Yet controlled evidence for linear concept structure and local mechanisms does not establish that these directions represent the intended behavioral concepts, leaving probing and steering without a unified theoretical account. We propose the Answer-Basin Representation Hypothesis (ABRH): the model's own answer measure organizes the linear structure of these directions. For each question, all continuations yielding the same answer form an answer basin, whose mass is their total probability; these masses define the model's answer distribution. ABRH posits that its concentration before generation and the relative mass of each answer after generation are represented along linear directions shared across questions. Experiments span four Qwen2.5 and Gemma-3 models on correctness, social bias, and safety tasks. Decoupling concept labels from basin mass shows that probing and steering exhibit concept-consistent effects when labels align with mass orderings, weaken as mass gaps shrink, and reverse under conflict. Probes trained solely on mass orderings among wrong answers still select correct answers; a fixed steering vector can instead favor wrong answers when labels conflict with mass orderings. These results support an answer-measure account of behavior-level concept directions and of when probing and steering succeed, fail, or reverse.

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References · 21

  1. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  3. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  4. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,259
  5. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,239
  6. 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,009
  7. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 960
  8. 2023Steering Language Models With Activation Engineeringcited by 824
  9. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 795
  10. 2022Discovering Latent Knowledge in Language Models Without Supervisioncited by 733
  11. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 683
  12. 2023The Linear Representation Hypothesis and the Geometry of Large Language Modelscited by 679
9 more
  1. 2023The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasetscited by 673
  2. 2023The Internal State of an LLM Knows When It's Lyingcited by 644
  3. 2024Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMscited by 246
  4. 2023SafetyBench: Evaluating the Safety of Large Language Modelscited by 189
  5. 2024The Geometry of Categorical and Hierarchical Concepts in Large Language Modelscited by 151
  6. 2024On the Origins of Linear Representations in Large Language Modelscited by 76
  7. 2023Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness?cited by 62
  8. 2025No Answer Needed: Predicting LLM Answer Accuracy from Question-Only Linear Probescited by 35
  9. 2026The Information Geometry of Softmax: Probing and Steeringcited by 9

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