LLM Interpretability

LLM: Large Language Model

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57 papers in the last four weeks, up 73% on the four weeks before. 0.6% of all new papers.

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Investigating Linguistic Steering: An Analysis of Adjectival Effects Across Large Language Model Architectures
Apr 28, 2026Lars MalmqvistPrompt Sensitivity Language Model Steering
Research and Implementation
Analysis and Explainability of LLMs Via Evolutionary Methods
Apr 27, 2026Shannon K. Gallagher, Swati Rallapalli, Tyler Brooks +3LLM Interpretability
Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA · Software Engineering Institute, AI Division, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Differentiable Faithfulness Alignment for Cross-Model Circuit Transfer
Apr 27, 2026Shun Shao, Binxu Wang, Shay B. Cohen +2LLM Interpretability Mechanistic Interpretability
University of Cambridge · Kempner Institute, Harvard University · University of Edinburgh +1
Knowledge Vector of Logical Reasoning in Large Language Models
Apr 26, 2026Zixuan Wang, Yuanyuan LeiLLM Interpretability Representation Engineering
Department of Computer & Information Science and Engineering University of Florida, Gainesville, FL, United States
Domain-Filtered Knowledge Graphs from Sparse Autoencoder Features
Apr 26, 2026John Winnicki, Abeynaya Gnanasekaran, Eric DarveKG Construction LLM Interpretability
Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
AIPsy-Affect: A Keyword-Free Clinical Stimulus Battery for Mechanistic Interpretability of Emotion in Language Models
Apr 26, 2026Michael KeemanLLM Interpretability Emotion Representation in Language Models
Keido Labs
Ulterior Motives: Detecting Misaligned Reasoning in Continuous Thought Models
Apr 25, 2026Sharan RamjeeLLM Backdoor Attacks Moral Reasoning in Language Models
Stanford University
From Concept-Aligned Tokens to Vulnerable Features: Mechanistic Localization of Jailbreaks
Apr 25, 2026Nilanjana Das, Mathew Dawit, Aman Chadha +1LLM Interpretability Sparse Autoencoders
UMBC, Maryland, U.S.A. · Apple, California, U.S.A.
Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization
Apr 24, 2026Weixu Zhang, Ye Yuan, Changjiang Han +7LLM Alignment Language Model Steering
McGill University · Mila - Quebec AI Institute · MBZUAI +2
How LLMs Detect and Correct Their Own Errors: The Role of Internal Confidence Signals
Apr 24, 2026Dharshan Kumaran, Viorica Patraucean, Simon Osindero +2LLM Self-Correction Language Model Error Detection
Google DeepMind · Princeton University
Shared Lexical Task Representations Explain Behavioral Variability In LLMs
Apr 23, 2026Zhuonan Yang, Jacob Xiaochen Li, Francisco Piedrahita Velez +5Prompt Sensitivity LLM Prompting
Brown University · MIT · Northeastern
Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models
Apr 23, 2026Vipula Rawte, Ryan Rossi, Franck Dernoncourt +1LLM Answer Verification LLM Auditing
Adobe · Adobe Research
Slot Machines: How LLMs Keep Track of Multiple Entities
Apr 22, 2026Paul C. Bogdan, Jack LindseyLLM Interpretability Language Model Probing
Anthropic Fellows Program · Anthropic
TabSHAP
Apr 22, 2026Aryan Chaudhary, Prateek Agarwal, Tejasvi AlladiFeature Attribution SHAP Feature Attribution
Department of Computer Science Birla Institute of Technology and Science India
Breaking Bad: Interpretability-Based Safety Audits of State-of-the-Art LLMs
Apr 22, 2026Krishiv Agarwal, Ramneet Kaur, Colin Samplawski +6Language Model Safety Evaluation LLM Auditing
University of Florida · SRI
Dual-Stance Evaluation of Sycophancy: The Structure of Agreement and the Limits of Intervention
Apr 22, 2026Matthew James BuchanLLM Sycophancy Language Model Steering
Independent Researcher, United Kingdom.
LayerTracer: A Joint Task-Particle and Vulnerable-Layer Analysis framework for Arbitrary Large Language Model Architectures
Apr 22, 2026Yuhang Wu, Qinyuan Liu, Qiuyang Zhao +1LLM Interpretability Language Model Robustness
China Electronic Technology Nanhu Research Institute, Jiaxing, China
Surrogate modeling for interpreting black-box LLMs in medical predictions
Apr 22, 2026Changho Han, Songsoo Kim, Dong Won Kim +4Surrogate Modeling LLM Interpretability
Medical Big Data Research Center, Seoul National University Medical Research Center, Seoul National University College of Medicine, Seoul, Republic of Korea · Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea. · Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02139 +5
R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling
Apr 22, 2026Aijia Cheng, Kailong Wang, Ling Shi +1RL for Language Models LLM Interpretability
Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China · Huazhong University of Science and Technology · Nanyang Technological University
Are LLM Uncertainty and Correctness Encoded by the Same Features? A Functional Dissociation via Sparse Autoencoders
Apr 21, 2026Het Patel, Tiejin Chen, Hua Wei +2LLM Interpretability LLM Reliability
University of California, Riverside, CA · Arizona State University, Tempe, AZ
How Do Answer Tokens Read Reasoning Traces? Self-Reading Patterns in Thinking LLMs for Quantitative Reasoning
Apr 21, 2026Haoyang Chen, Yi Liu, Jianzhi Shao +3LLM Interpretability Mathematical Reasoning
State Key Laboratory for Novel Software Technology, Nanjing University, China · Alibaba Group, China · Ant Group, China
Cell-Based Representation of Relational Binding in Language Models
Apr 21, 2026Qin Dai, Benjamin Heinzerling, Kentaro InuiLLM Interpretability Representation Probing
1Tohoku University · 2RIKEN AIP · 3MBZUAI
Harmful Intent as a Geometrically Recoverable Feature of LLM Residual Streams
Apr 20, 2026Isaac Llorente-SaguerLanguage Model Safety Evaluation LLM Interpretability
Independent Researcher
Different Paths to Harmful Compliance: Behavioral Side Effects and Mechanistic Divergence Across LLM Jailbreaks
Apr 20, 2026Md Rysul Kabir, Zoran TiganjLanguage Model Safety Evaluation LLM Interpretability
Department of Computer Science Luddy School of Informatics, Computing, and Engineering Indiana University Bloomington
Understanding the Prompt Sensitivity
Apr 20, 2026Yang Liu, Chenhui ChuPrompt Sensitivity LLM Prompting
Kyoto University
Reasoning Models Know What's Important, and Encode It in Their Activations
Apr 20, 2026Yaniv Nikankin, Martin Tutek, Tomer Ashuach +2LLM Interpretability LLM Reasoning
Technion · University of Zagreb, FER · MIT +1
Exploring Concreteness Through a Figurative Lens
Apr 20, 2026Saptarshi Ghosh, Tianyu JiangFigurative Language Understanding LLM Interpretability
University of Cincinnati
Polysemantic Experts, Monosemantic Paths: Routing as Control in MoEs
Apr 20, 2026Charles Ye, Bo Yuan, Lee SharkeyLLM Interpretability Neural Network Interpretability
Independent · Georgia Institute of Technology · Goodfire
Contrastive Attribution in the Wild: An Interpretability Analysis of LLM Failures on Realistic Benchmarks
Apr 20, 2026Rongyuan Tan, Jue Zhang, Zhuozhao Li +3LLM Evaluation Feature Attribution
1Southern University of Science and Technology, China · *Work done during an internship at Microsoft. · 2Microsoft
Language models recognize dropout and Gaussian noise applied to their activations
Apr 19, 2026Damiano Fornasiere, Mirko Bronzi, Spencer Kitts +3LLM Interpretability Language Model Introspection
LawZero

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Investigating Linguistic Steering: An Analysis of Adjectival Effects Across Large Language Model Architectures

Lars Malmqvist

Research and Implementation

Prompt Sensitivity Language Model Steering LLM Prompting LLM Interpretability

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Achieving reliable control of Large Language Models (LLMs) requires a precise, scalable understanding of how they interpret linguistic cues. We introduce a rigorous framework using Shapley values to quantify the steering effect of individual adjectives on model performance, moving beyond anecdotal heuristics to principled attribution. Applying this method to 100 adjectives across a diverse suite of models (including o3, gpt-4o-mini, phi-3, llama-3-70b, and deepseek-r1) on the MMLU benchmark, we uncover several critical findings for AI alignment. First, we find that a small subset of adjectives act as disproportionately powerful "levers," yet their effects are not universal. Cross-model analysis reveals a "family effect": models of a shared lineage exhibit correlated sensitivity profiles, while architecturally distinct models react in a largely uncorrelated manner, challenging the notion of a one-size-fits-all prompting strategy. Second, focused follow-up studies demonstrate that the steering direction of these powerful adjectives is not intrinsic but is highly contingent on their syntactic role and position within the prompt. For larger models like gpt-4o-mini, we provide the first quantitative evidence of strong, non-additive interaction effects where adjectives can synergistically amplify, antagonistically dampen, or even reverse each other's impact. In contrast, smaller models like phi-3 exhibit a more literal and less compositional response. These results suggest that as models scale, their interpretation of prompts becomes more sophisticated but also less predictable, posing a significant challenge for robustly steering model behavior and highlighting the need for compositional and model-specific alignment techniques.

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Latest papers 546