arXiv:2601.04765v4 Announce Type: replace-cross Abstract: We study how syntactic and semantic information is encoded in inner layer representations of Large Language Models (LLMs), focusing on the very large DeepSeek-V3. We find that, by averaging hidden-representation vectors of sentences sharing syntactic structure or meaning, we obtain vectors that capture a significant proportion of the syntactic and semantic information contained in the representations. In particular, subtracting these syntactic and semantic ``centroids'' from sentence vectors strongly affects their similarity with syntac
This research details a methodological breakthrough in understanding the internal mechanisms of LLMs, which is critical as models become larger and more opaque, published just as the industry is focused on explainability and efficiency.
Understanding how LLMs encode syntactic and semantic information directly advances their interpretability, robustness, and potential for targeted improvements, moving beyond black-box optimization.
The ability to isolate and manipulate specific types of linguistic information within LLM representations opens new avenues for fine-tuning, debugging, and potentially creating more precise and controllable AI systems.
- · AI researchers
- · LLM developers
- · Companies building on foundational models
- · AI ethics and safety organizations
- · Those relying solely on black-box LLM development
- · Less interpretable AI architectures
Improved understanding of LLM internal workings allows for more efficient and targeted model development.
Enhanced interpretability leads to the creation of more trustworthy and auditable AI systems, fostering greater adoption in sensitive applications.
The ability to 'subtract' specific information could lead to new forms of AI control, content filtering, or bias mitigation directly at the representation level.
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Read at arXiv cs.LG