Language Model Probing

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PROMPT2BOX: Uncovering Entailment Structure among LLM Prompts
Mar 22, 2026Neeladri Bhuiya, Shib Sankar Dasgupta, Andrew McCallum +1LLM Evaluation Representation Learning
CICS, University of Massachusetts Amherst, USA · Amazon AWS AI
World Properties without World Models: Distributional Associations and the Interpretation of Decoding Results from Language Models
Mar 4, 2026Elan BarenholtzLLM World Models Word Embeddings
Department of Psychology & Center for Complex Systems and Brain Sciences Florida Atlantic University
Probing for Knowledge Attribution in Large Language Models
Feb 26, 2026Ivo Brink, Alexander Boer, Dennis UlmerLLM Interpretability Hallucination in Language Models
KPMG NL / University of Amsterdam Amsterdam, The Netherlands · KPMG NL Amsterdam, The Netherlands · University of Amsterdam Amsterdam, The Netherlands
How Transformers Reject Wrong Answers: Rotational Dynamics of Factual Constraint Processing
Feb 25, 2026Javier MarínTransformer Interpretability LLM Interpretability
Groundlens-dev
Rethinking LLM-as-a-Judge: Representation-as-a-Judge with Small Language Models via Semantic Capacity Asymmetry
Jan 30, 2026Zhuochun Li, Yong Zhang, Ming Li +8LLM Evaluation LLM-as-a-Judge
Ping An Technology (Shenzhen) Co., Ltd. · University of Pittsburgh · University of Maryland, College Park +1
BoRP: Bootstrapped Regression Probing for Scalable and Human-Aligned LLM Evaluation
Jan 26, 2026Peng Sun, Xiangyu Zhang, Duan Wu +5LLM Evaluation Representation Probing
Qwen Business Unit of Alibaba
Training-free Truthfulness Detection via Sparse MLP Value Vectors
Sep 22, 2025Runheng Liu, Heyan Huang, Xingchen Xiao +2LLM Reliability LLM Hallucination Detection
Beijing Institute of Technology Beijing, China
Human Psychometric Questionnaires Mischaracterize LLM Behavior
Sep 12, 2025Woojung Song, Dongmin Choi, Yoonah Park +3Personality Modeling in Language Models Psychometric Validation
1Graduate School of Data Science, Seoul National University · Department of Communication, Interdisciplinary Program in Artificial Intelligence, Seoul National University
The Trilemma of Truth in Large Language Models
Jun 30, 2025Germans Savcisens, Tina Eliassi-RadLLM Interpretability LLM Reliability
Khoury College of Computer Sciences Northeastern University, Boston, MA, USA · Network Science Institute Northeastern University, Boston, MA, USA · Santa Fe Institute, Santa Fe, NM, USA
Cross-Layer Discrete Concept Discovery for Interpreting Language Models
Jun 24, 2025Ankur Garg, Xuemin Yu, Hassan Sajjad +1Variational Autoencoders LLM Interpretability
Evaluating LLMs on Chinese Topic Constructions: A Research Proposal Inspired by Tian et al. (2024)
Apr 21, 2025Xiaodong YangLLM Evaluation Language Model Probing
College of Foreign Languages Zhejiang University of Technology 288, Liuhe Rd., Xihu District, Hangzhou, Zhejiang, 310023 China
Listening to the Wise Few: Query-Key Alignment Unlocks Latent Correct Answers in Large Language Models
Oct 3, 2024Eduard Tulchinskii, Kristian Kuznetsov, Laida Kushnareva +5Self-Attention Multiple-Choice Question Answering
Skolkovo Institute of Science and Technology · AI Foundation and Algorithm Lab · Moscow Institute of Physics and Technology +2

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PROMPT2BOX: Uncovering Entailment Structure among LLM Prompts

Neeladri Bhuiya, Shib Sankar Dasgupta, Andrew McCallum, Haw-Shiuan Chang

CICS, University of Massachusetts Amherst, USA · Amazon AWS AI

LLM Evaluation Representation Learning Language Model Probing

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To discover the weaknesses of LLMs, researchers often embed prompts into a vector space and cluster them to extract insightful patterns. However, vector embeddings primarily capture topical similarity. As a result, prompts that share a topic but differ in specificity, and consequently in difficulty, are often represented similarly, making fine-grained weakness analysis difficult. To address this limitation, we propose PROMPT2BOX, which embeds prompts into a box embedding space using a trained encoder. The encoder, trained on existing and synthesized datasets, outputs box embeddings that capture not only semantic similarity but also specificity relations between prompts (e.g., "writing an adventure story" is more specific than "writing a story"). We further develop a novel dimension reduction technique for box embeddings to facilitate dataset visualization and comparison. Our experiments demonstrate that box embeddings consistently capture prompt specificity better than vector baselines. On the downstream task of creating hierarchical clustering trees for 17 LLMs from the UltraFeedback dataset, PROMPT2BOX can identify 8.9% more LLM weaknesses than vector baselines and achieves an approximately 33% stronger correlation between hierarchical depth and instruction specificity.

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