Where does an LLM begin computing an instruction?
Not on the map. Why?
Abstract (arXiv)
Following an instruction involves distinct sub-processes, such as reading content, reading the instruction, executing it, and producing an answer. We ask where, along the layer stack, instruction following begins, the point where reading gives way to doing. We introduce three simple datasets (Key-Value, Quote Attribution, Letter Selection) and two hop compositions of these tasks. Using activation patching on minimal-contrast prompt pairs, we measure a layer-wise flip rate that indicates when substituting selected residual activations changes the predicted answer. Across models in the Llama family, we observe an inflection point, which we term onset, where interventions that change predictions before this point become largely ineffective afterward. Multi-hop compositions show a similar onset location. These results provide a simple, replicable way to locate where instruction following begins and to compare this location across tasks and model sizes.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 12 Nov 2025.
Builds on
8 of 14 references.
References · 8
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
- 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
- 2022Locating and Editing Factual Associations in GPTcited by 2,691
- 2023Instruction-Following Evaluation for Large Language Modelscited by 1,228
- 2022In-context Learning and Induction Headscited by 964
- 2024How to use and interpret activation patchingcited by 214
- 2024Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactualscited by 40
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2024Do LLMs "know" internally when they follow instructions?
- 2026RIFT: Reordered Instruction Following Testbed To Evaluate Instruction Following in Singular Multistep Prompt Structures
- 2026Do as I Say, Not as I Do: Instruction-Induction Conflict in LLMs
- 2025Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities
- 2023FollowEval: A Multi-Dimensional Benchmark for Assessing the Instruction-Following Capability of Large Language Models
- 2024KCIF: Knowledge-Conditioned Instruction Following
- 2025AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following
- 2025The Instruction Gap: LLMs get lost in Following Instruction