Papers by Nicolas Boulle
What’s in a prompt? Language models encode literary style in prompt embeddings (2025.emnlp-main)
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| Challenge: | Large language models encode textual information using high-dimensional latent spaces . many studies have investigated how conceptual content of words translates into geometrical relationships between their vector representations . |
| Approach: | They use literary pieces to show that intangible, rather than factual, aspects of the prompt are contained in deep representations. |
| Outcome: | The results show that word-to-vec(tor) embeddings are more complex than other models. |
LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law (2024.emnlp-main)
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| Challenge: | LLaMA-2 language model is capable of in-context time series extrapolation without specific prompting or fine-tuning, revealing an in-constitution version of a neural scaling law. |
| Approach: | They propose an algorithm for extracting probability density functions of multi-digit numbers directly from Large language models (LLMs) LLaMA-2 is a language model trained on text and can extrapolate dynamical system time series without prompting or engineering . |
| Outcome: | The proposed model can extrapolate dynamical systems without prompting or engineering . it also achieves an in-context version of a neural scaling law . |