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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Accès en ligne: | https://arxiv.org/abs/2505.14530 |
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| _version_ | 1866916973897580544 |
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| author | Yang, Zhipeng Li, Junzhuo Xia, Siyu Hu, Xuming |
| author_facet | Yang, Zhipeng Li, Junzhuo Xia, Siyu Hu, Xuming |
| contents | We show that large language models (LLMs) exhibit an $\textit{internal chain-of-thought}$: they sequentially decompose and execute composite tasks layer-by-layer. Two claims ground our study: (i) distinct subtasks are learned at different network depths, and (ii) these subtasks are executed sequentially across layers. On a benchmark of 15 two-step composite tasks, we employ layer-from context-masking and propose a novel cross-task patching method, confirming (i). To examine claim (ii), we apply LogitLens to decode hidden states, revealing a consistent layerwise execution pattern. We further replicate our analysis on the real-world $\text{TRACE}$ benchmark, observing the same stepwise dynamics. Together, our results enhance LLMs transparency by showing their capacity to internally plan and execute subtasks (or instructions), opening avenues for fine-grained, instruction-level activation steering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14530 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Internal Chain-of-Thought: Empirical Evidence for Layer-wise Subtask Scheduling in LLMs Yang, Zhipeng Li, Junzhuo Xia, Siyu Hu, Xuming Computation and Language Machine Learning We show that large language models (LLMs) exhibit an $\textit{internal chain-of-thought}$: they sequentially decompose and execute composite tasks layer-by-layer. Two claims ground our study: (i) distinct subtasks are learned at different network depths, and (ii) these subtasks are executed sequentially across layers. On a benchmark of 15 two-step composite tasks, we employ layer-from context-masking and propose a novel cross-task patching method, confirming (i). To examine claim (ii), we apply LogitLens to decode hidden states, revealing a consistent layerwise execution pattern. We further replicate our analysis on the real-world $\text{TRACE}$ benchmark, observing the same stepwise dynamics. Together, our results enhance LLMs transparency by showing their capacity to internally plan and execute subtasks (or instructions), opening avenues for fine-grained, instruction-level activation steering. |
| title | Internal Chain-of-Thought: Empirical Evidence for Layer-wise Subtask Scheduling in LLMs |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2505.14530 |