mlsanityData to 14 Oct 2026

Extracting Paragraphs from LLM Token Activations

arXiv 2409.06328 ↗First posted 10 Sep 2024cs.CLcited by 9

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Abstract (arXiv)

Generative large language models (LLMs) excel in natural language processing tasks, yet their inner workings remain underexplored beyond token-level predictions. This study investigates the degree to which these models decide the content of a paragraph at its onset, shedding light on their contextual understanding. By examining the information encoded in single-token activations, specifically the "textbackslash ntextbackslash n" double newline token, we demonstrate that patching these activations can transfer significant information about the context of the following paragraph, providing further insights into the model's capacity to plan ahead.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 24 of its 30 references that are in the map. First posted on arXiv 10 Sep 2024.

Builds on

24 of 30 references.

References · 24

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12 more
  1. 2024Better & Faster Large Language Models via Multi-token Predictioncited by 360
  2. 2023Towards Best Practices of Activation Patching in Language Models: Metrics and Methodscited by 359
  3. 2024Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Modelscited by 235
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  5. 2023Inspecting and Editing Knowledge Representations in Language Modelscited by 141
  6. 2023Future Lens: Anticipating Subsequent Tokens from a Single Hidden Statecited by 121
  7. 2023AnglE-optimized Text Embeddingscited by 99
  8. 2024Do language models plan ahead for future tokens?cited by 49
  9. 2024Speculative Streaming: Fast LLM Inference without Auxiliary Modelscited by 38
  10. 2024Unlocking the Future: Exploring Look-Ahead Planning Mechanistic Interpretability in Large Language Modelscited by 22
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  12. 2023Language Models are Universal Embedderscited by 11

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 4.

9 papers.

  1. 2025Overcoming Sparsity Artifacts in Crosscoders to Interpret Chat-Tuningcited by 34
  2. 2025Emergent Response Planning in LLMscited by 19
  3. 2025Language Models Can Predict Their Own Behaviorcited by 8
  4. 2026What's the plan? Metrics for implicit planning in LLMs and their application to rhyme generation and question answeringcited by 6
  5. 2025Detecting and Characterizing Planning in Language Modelscited by 2
  6. 2025ParaScopes: What do Language Models Activations Encode About Future Text?cited by 2
  7. 2026Do Large Language Models Plan Answer Positions? Position Bias in Multiple-Choice Question Generationcited by 1
  8. 2026Script Choice in LLMs: Evidence for Late-Layer Commitmentcited by 1
  9. 2026Large Language Models Can Take False First Steps at Inference-time Planningcited by 0