mlsanityData to 7 Oct 2026

How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study

arXiv 2402.16061 ↗First posted 25 Feb 2024cs.CLcited by 64

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Abstract (arXiv)

Previous work has showcased the intriguing capability of large language models (LLMs) in retrieving facts and processing context knowledge. However, only limited research exists on the layer-wise capability of LLMs to encode knowledge, which challenges our understanding of their internal mechanisms. In this paper, we devote the first attempt to investigate the layer-wise capability of LLMs through probing tasks. We leverage the powerful generative capability of ChatGPT to construct probing datasets, providing diverse and coherent evidence corresponding to various facts. We employ mathcal V-usable information as the validation metric to better reflect the capability in encoding context knowledge across different layers. Our experiments on conflicting and newly acquired knowledge show that LLMs: (1) prefer to encode more context knowledge in the upper layers; (2) primarily encode context knowledge within knowledge-related entity tokens at lower layers while progressively expanding more knowledge within other tokens at upper layers; and (3) gradually forget the earlier context knowledge retained within the intermediate layers when provided with irrelevant evidence. Code is publicly available at https://github.com/Jometeorie/probingllama.

Key facts. Cited by 64 papers in the map (data through 7 Oct 2026). Builds on 27 of its 41 references that are in the map. First posted on arXiv 25 Feb 2024.

Builds on

27 of 41 references.

References · 27

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  4. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  5. 2022Locating and Editing Factual Associations in GPTcited by 2,609
  6. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,734
  7. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  8. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  9. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
  10. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 831
  11. 2020A Primer in BERTology: What we know about how BERT workscited by 807
  12. 2023C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Modelscited by 694
15 more
  1. 2019Designing and Interpreting Probes with Control Taskscited by 608
  2. 2018What you can cram into a single vector: Probing sentence embeddings for linguistic propertiescited by 452
  3. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 452
  4. 2019What do you learn from context? Probing for sentence structure in contextualized word representationscited by 436
  5. 2023Toxicity in ChatGPT: Analyzing Persona-assigned Language Modelscited by 356
  6. 2021Understanding Dataset Difficulty with V-Usable Informationcited by 288
  7. 2023Mathematical Capabilities of ChatGPTcited by 263
  8. 2019Analyzing the Structure of Attention in a Transformer Language Modelcited by 241
  9. 2023Do Large Language Models Know What They Don't Know?cited by 238
  10. 2020Information-Theoretic Probing for Linguistic Structurecited by 204
  11. 2022A Review on Language Models as Knowledge Basescited by 156
  12. 2020A Theory of Usable Information Under Computational Constraintscited by 153
  13. 2023Physics of Language Models: Part 3.2, Knowledge Manipulationcited by 142
  14. 2023Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformercited by 101
  15. 2021Exploring the Role of BERT Token Representations to Explain Sentence Probing Resultscited by 29

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Citing papers per year: 2024: 13; 2025: 31; 2026: 20.

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  1. 2024Defending Large Language Models Against Jailbreak Attacks via Layer-specific Editingcited by 77
  2. 2024Knowledge Mechanisms in Large Language Models: A Survey and Perspectivecited by 50
  3. 2024Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource Languagescited by 38
  4. 2024Insights into LLM Long-Context Failures: When Transformers Know but Don't Tellcited by 36
  5. 2025CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignmentcited by 22
  6. 2026Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Modelscited by 22
  7. 2024Pooling And Attention: What Are Effective Designs For LLM-Based Embedding Models?cited by 19
  8. 2025Representation Engineering for Large-Language Models: Survey and Research Challengescited by 18
  9. 2025Vision Function Layer in Multimodal LLMscited by 17
  10. 2025False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalizecited by 15
  11. 2024AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deploymentcited by 12
  12. 2024Exploring Multilingual Probing in Large Language Models: A Cross-Language Analysiscited by 11
38 more
  1. 2025HBridge: H-Shape Bridging of Heterogeneous Experts for Unified Multimodal Understanding and Generationcited by 11
  2. 2025Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safetycited by 10
  3. 2025Probing then Editing Response Personality of Large Language Modelscited by 9
  4. 2024What Do Speech Foundation Models Not Learn About Speech?cited by 8
  5. 2025EVA: Evolving Semantic Adversaries for Red-Teaming GUI Agents Against Environmental Injection Attackscited by 8
  6. 2025HiProbe-VAD: Video Anomaly Detection via Hidden States Probing in Tuning-Free Multimodal LLMscited by 8
  7. 2024Explainable AI for Mental Disorder Detection via Social Media: A survey and outlookcited by 6
  8. 2025Layerwise Recall and the Geometry of Interwoven Knowledge in LLMscited by 5
  9. 2026ATACompressor: Adaptive Task-Aware Compression for Efficient Long-Context Processing in LLMscited by 5
  10. 2024Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?cited by 4
  11. 2024Selected Languages are All You Need for Cross-lingual Truthfulness Transfercited by 4
  12. 2024An exploration of the effect of quantisation on energy consumption and inference time of StarCoder2cited by 4
  13. 2025Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Setcited by 4
  14. 2025Probing Neural Topology of Large Language Modelscited by 4
  15. 2025HARP: Hallucination Detection via Reasoning Subspace Projectioncited by 4
  16. 2025LLM Hallucination Detection: A Fast Fourier Transform Method Based on Hidden Layer Temporal Signalscited by 4
  17. 2026Identifying and Transferring Reasoning-Critical Neurons: Improving LLM Inference Reliability via Activation Steeringcited by 4
  18. 2025Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language Modelscited by 3
  19. 2025Localizing Persona Representations in LLMscited by 3
  20. 2025Bridging External and Parametric Knowledge: Mitigating Hallucination of LLMs with Shared-Private Semantic Synergy in Dual-Stream Knowledgecited by 3
  21. 2025The Compositional Architecture of Regret in Large Language Modelscited by 3
  22. 2025Harmonious Parameter Adaptation in Continual Visual Instruction Tuning for Safety-Aligned MLLMscited by 3
  23. 2026Where Does Vision Meet Language? Understanding and Refining Visual Fusion in MLLMs via Contrastive Attentioncited by 3
  24. 2026Where to Steer: Input-Dependent Layer Selection for Steering Improves LLM Alignmentcited by 3
  25. 2026Navigating by Old Maps: The Pitfalls of Static Mechanistic Localization in LLM Post-Trainingcited by 3
  26. 2025Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scalecited by 2
  27. 2025Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoningcited by 2
  28. 2025Is In-Context Learning Learning?cited by 2
  29. 2025HeadHunt-VAD: Hunting Robust Anomaly-Sensitive Heads in MLLM for Tuning-Free Video Anomaly Detectioncited by 2
  30. 2024Past Meets Present: Creating Historical Analogy with Large Language Modelscited by 1
  31. 2025Exploiting Contextual Knowledge in LLMs through V-usable Information based Layer Enhancementcited by 1
  32. 2025Void in Language Modelscited by 1
  33. 2025Plug-in and Fine-tuning: Bridging the Gap between Small Language Models and Large Language Modelscited by 1
  34. 2025LLM Hallucination Detection: HSADcited by 1
  35. 2025STEAM: A Semantic-Level Knowledge Editing Framework for Large Language Modelscited by 1
  36. 2026A Shared Geometry of Difficulty in Multilingual Language Modelscited by 1
  37. 2026UniCog: Uncovering Cognitive Abilities of LLMs through Latent Mind Space Analysiscited by 1
  38. 2026Golden Layers and Where to Find Them: Improved Knowledge Editing for Large Language Models Via Layer Gradient Analysiscited by 1

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