Hierarchical Latent Structures in Data Generation Process Unify Mechanistic Phenomena across Scale
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arXiv
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| Natura: | Preprint |
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2026
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| author | Rohweder, Jonas Dutta, Subhabrata Gurevych, Iryna |
| author_facet | Rohweder, Jonas Dutta, Subhabrata Gurevych, Iryna |
| contents | Contemporary studies have uncovered many puzzling phenomena in the neural information processing of Transformer-based language models. Building a robust, unified understanding of these phenomena requires disassembling a model within the scope of its training. While the intractable scale of pretraining corpora limits a bottom-up investigation in this direction, simplistic assumptions of the data generation process limit the expressivity and fail to explain complex patterns. In this work, we use probabilistic context-free grammars (PCFGs) to generate synthetic corpora that are faithful and computationally efficient proxies for web-scale text corpora. We investigate the emergence of three mechanistic phenomena: induction heads, function vectors, and the Hydra effect, under our designed data generation process, as well as in the checkpoints of real-world language models. Our findings suggest that hierarchical structures in the data generation process serve as the X-factor in explaining the emergence of these phenomena. We provide the theoretical underpinnings of the role played by hierarchy in the training dynamics of language models. In a nutshell, our work is the first of its kind to provide a unified explanation behind the emergence of seemingly unrelated mechanistic phenomena in LLMs, augmented with efficient synthetic tooling for future interpretability research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06592 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hierarchical Latent Structures in Data Generation Process Unify Mechanistic Phenomena across Scale Rohweder, Jonas Dutta, Subhabrata Gurevych, Iryna Computation and Language Machine Learning Contemporary studies have uncovered many puzzling phenomena in the neural information processing of Transformer-based language models. Building a robust, unified understanding of these phenomena requires disassembling a model within the scope of its training. While the intractable scale of pretraining corpora limits a bottom-up investigation in this direction, simplistic assumptions of the data generation process limit the expressivity and fail to explain complex patterns. In this work, we use probabilistic context-free grammars (PCFGs) to generate synthetic corpora that are faithful and computationally efficient proxies for web-scale text corpora. We investigate the emergence of three mechanistic phenomena: induction heads, function vectors, and the Hydra effect, under our designed data generation process, as well as in the checkpoints of real-world language models. Our findings suggest that hierarchical structures in the data generation process serve as the X-factor in explaining the emergence of these phenomena. We provide the theoretical underpinnings of the role played by hierarchy in the training dynamics of language models. In a nutshell, our work is the first of its kind to provide a unified explanation behind the emergence of seemingly unrelated mechanistic phenomena in LLMs, augmented with efficient synthetic tooling for future interpretability research. |
| title | Hierarchical Latent Structures in Data Generation Process Unify Mechanistic Phenomena across Scale |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2603.06592 |