The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets
Fuente:
arXiv
Guardado en:
| Autores principales: | Marks, Samuel, Tegmark, Max |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Language Models Represent Space and Time
por: Gurnee, Wes, et al.
Publicado: (2023)
por: Gurnee, Wes, et al.
Publicado: (2023)
Language Models Use Trigonometry to Do Addition
por: Kantamneni, Subhash, et al.
Publicado: (2025)
por: Kantamneni, Subhash, et al.
Publicado: (2025)
Investigating Representation Universality: Case Study on Genealogical Representations
por: Baek, David D., et al.
Publicado: (2024)
por: Baek, David D., et al.
Publicado: (2024)
Neural Thermodynamic Laws for Large Language Model Training
por: Liu, Ziming, et al.
Publicado: (2025)
por: Liu, Ziming, et al.
Publicado: (2025)
The Linear Representation Hypothesis and the Geometry of Large Language Models
por: Park, Kiho, et al.
Publicado: (2023)
por: Park, Kiho, et al.
Publicado: (2023)
The Geometry of Concepts: Sparse Autoencoder Feature Structure
por: Li, Yuxiao, et al.
Publicado: (2024)
por: Li, Yuxiao, et al.
Publicado: (2024)
Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models
por: Liang, Kaiqu, et al.
Publicado: (2025)
por: Liang, Kaiqu, et al.
Publicado: (2025)
Emergent Structured Representations Support Flexible In-Context Inference in Large Language Models
por: Xu, Ningyu, et al.
Publicado: (2026)
por: Xu, Ningyu, et al.
Publicado: (2026)
Overthinking the Truth: Understanding how Language Models Process False Demonstrations
por: Halawi, Danny, et al.
Publicado: (2023)
por: Halawi, Danny, et al.
Publicado: (2023)
A Neural Scaling Law from Lottery Ticket Ensembling
por: Liu, Ziming, et al.
Publicado: (2023)
por: Liu, Ziming, et al.
Publicado: (2023)
Do Two AI Scientists Agree?
por: Fu, Xinghong, et al.
Publicado: (2025)
por: Fu, Xinghong, et al.
Publicado: (2025)
Emergent Hierarchical Structure in Large Language Models: An Information-Theoretic Framework for Multi-Scale Representation
por: Zhang, Yukin, et al.
Publicado: (2025)
por: Zhang, Yukin, et al.
Publicado: (2025)
Convergent Linear Representations of Emergent Misalignment
por: Soligo, Anna, et al.
Publicado: (2025)
por: Soligo, Anna, et al.
Publicado: (2025)
Context Structure Reshapes the Representational Geometry of Language Models
por: Hosseini, Eghbal A., et al.
Publicado: (2026)
por: Hosseini, Eghbal A., et al.
Publicado: (2026)
Measuring Representation Robustness in Large Language Models for Geometry
por: Jawandhia, Vedant, et al.
Publicado: (2026)
por: Jawandhia, Vedant, et al.
Publicado: (2026)
Zero-Shot Referring Expression Comprehension via Vison-Language True/False Verification
por: Liu, Jeffrey, et al.
Publicado: (2025)
por: Liu, Jeffrey, et al.
Publicado: (2025)
A Resource Model For Neural Scaling Law
por: Song, Jinyeop, et al.
Publicado: (2024)
por: Song, Jinyeop, et al.
Publicado: (2024)
Uncovering Emergent Physics Representations Learned In-Context by Large Language Models
por: Song, Yeongwoo, et al.
Publicado: (2025)
por: Song, Yeongwoo, et al.
Publicado: (2025)
How Do Transformers "Do" Physics? Investigating the Simple Harmonic Oscillator
por: Kantamneni, Subhash, et al.
Publicado: (2024)
por: Kantamneni, Subhash, et al.
Publicado: (2024)
TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space
por: Zhang, Shaolei, et al.
Publicado: (2024)
por: Zhang, Shaolei, et al.
Publicado: (2024)
TruthStance: An Annotated Dataset of Conversations on Truth Social
por: Ameen, Fathima, et al.
Publicado: (2026)
por: Ameen, Fathima, et al.
Publicado: (2026)
Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without Tuning
por: Chen, Zhongzhi, et al.
Publicado: (2023)
por: Chen, Zhongzhi, et al.
Publicado: (2023)
Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution
por: Wijegunarathna, Kalana, et al.
Publicado: (2025)
por: Wijegunarathna, Kalana, et al.
Publicado: (2025)
Liars' Bench: Evaluating Lie Detectors for Language Models
por: Kretschmar, Kieron, et al.
Publicado: (2025)
por: Kretschmar, Kieron, et al.
Publicado: (2025)
Large Language Models Can Take False First Steps at Inference-time Planning
por: Yan, Haijiang, et al.
Publicado: (2026)
por: Yan, Haijiang, et al.
Publicado: (2026)
Unlocking Emergent Modularity in Large Language Models
por: Qiu, Zihan, et al.
Publicado: (2023)
por: Qiu, Zihan, et al.
Publicado: (2023)
Emergent Introspective Awareness in Large Language Models
por: Lindsey, Jack
Publicado: (2026)
por: Lindsey, Jack
Publicado: (2026)
Unsupervised decoding of encoded reasoning using language model interpretability
por: Fang, Ching, et al.
Publicado: (2025)
por: Fang, Ching, et al.
Publicado: (2025)
TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliability
por: Khatun, Aisha, et al.
Publicado: (2024)
por: Khatun, Aisha, et al.
Publicado: (2024)
Latent Structure of Affective Representations in Large Language Models
por: Choi, Benjamin J., et al.
Publicado: (2026)
por: Choi, Benjamin J., et al.
Publicado: (2026)
The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence
por: Wollschläger, Tom, et al.
Publicado: (2025)
por: Wollschläger, Tom, et al.
Publicado: (2025)
Ranking Large Language Models without Ground Truth
por: Dhurandhar, Amit, et al.
Publicado: (2024)
por: Dhurandhar, Amit, et al.
Publicado: (2024)
Are Sparse Autoencoders Useful? A Case Study in Sparse Probing
por: Kantamneni, Subhash, et al.
Publicado: (2025)
por: Kantamneni, Subhash, et al.
Publicado: (2025)
Towards Reliable Truth-Aligned Uncertainty Estimation in Large Language Models
por: Srey, Ponhvoan, et al.
Publicado: (2026)
por: Srey, Ponhvoan, et al.
Publicado: (2026)
Why Cannot Large Language Models Ever Make True Correct Reasoning?
por: Cheng, Jingde
Publicado: (2025)
por: Cheng, Jingde
Publicado: (2025)
Steering Evaluation-Aware Language Models to Act Like They Are Deployed
por: Hua, Tim Tian, et al.
Publicado: (2025)
por: Hua, Tim Tian, et al.
Publicado: (2025)
The Remarkable Robustness of LLMs: Stages of Inference?
por: Lad, Vedang, et al.
Publicado: (2024)
por: Lad, Vedang, et al.
Publicado: (2024)
Scaling Laws For Scalable Oversight
por: Engels, Joshua, et al.
Publicado: (2025)
por: Engels, Joshua, et al.
Publicado: (2025)
To Tell The Truth: Language of Deception and Language Models
por: Hazra, Sanchaita, et al.
Publicado: (2023)
por: Hazra, Sanchaita, et al.
Publicado: (2023)
Art or Artifice? Large Language Models and the False Promise of Creativity
por: Chakrabarty, Tuhin, et al.
Publicado: (2023)
por: Chakrabarty, Tuhin, et al.
Publicado: (2023)
Ejemplares similares
-
Language Models Represent Space and Time
por: Gurnee, Wes, et al.
Publicado: (2023) -
Language Models Use Trigonometry to Do Addition
por: Kantamneni, Subhash, et al.
Publicado: (2025) -
Investigating Representation Universality: Case Study on Genealogical Representations
por: Baek, David D., et al.
Publicado: (2024) -
Neural Thermodynamic Laws for Large Language Model Training
por: Liu, Ziming, et al.
Publicado: (2025) -
The Linear Representation Hypothesis and the Geometry of Large Language Models
por: Park, Kiho, et al.
Publicado: (2023)