Research finds script knowledge in LLMs emerges only in final layers
A new paper on arXiv explores how large language models process script knowledge across their layers. Using logistic regression probing and logit-lens analysis, researchers found that input and instructed output scripts are encoded early in the network. However, the model’s commitment to the actual output script only solidifies in the final layers, with intermediate layers defaulting to Latin…
Key points
- Researchers used logistic regression probing and logit-lens analysis to study script knowledge distribution in LLMs
- Input and instructed output scripts appear in early layers, but final layers determine actual script commitment
- Smaller models show weaker script-following performance, hinting at a depth-related pattern
The study also notes weaker script-following performance in smaller models, suggesting a link between script commitment and model depth. The findings could inform the design of deeper, more inclusive multilingual architectures. The paper does not include external validation or benchmarks beyond the described methods.
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