arXiv subjects

Louis Jalouzot

Publications and source records attributed to Louis Jalouzot.

4 recordsLinked to original sources

arXiv2026

What Makes Two Language Models Think Alike?

Do architectural and training differences influence the way models represent and process language? Traditional similarity metrics tell us whether two models share a similar representational geometry, but they cannot explain why. Here, we propose a new, simple, approach to address this question. This approach maps neural activity in each model layer onto a set of interpretable linguistic features and quantifies how much each of them drives similarities and differences between models. We use this approach to compare 43 language models across 10 families, including decoder Transformers, State-Space Models, and Recurrent Neural Networks. We find that model-level similarity is driven most strongly by release date, a proxy for general LLM development, and model family, suggesting that linguistic signatures are not primarily shaped by scale or architecture class. Overall, our approach provides a way to link theoretically-motivated symbolic descriptions to neural representations and can readily be extended to other domains such as speech and vision, and to other neural systems such as biological brains.

cs.CL↗

arXiv2025

Metric Learning Encoding Models: A Multivariate Framework for Interpreting Neural Representations

Understanding how explicit theoretical features are encoded in opaque neural systems is a central challenge now common to neuroscience and AI. We introduce Metric Learning Encoding Models (MLEMs) to address this challenge most directly as a metric learning problem: we fit the distance in the space of theoretical features to match the distance in neural space. Our framework improves on univariate encoding and decoding methods by building on second-order isomorphism methods, such as Representational Similarity Analysis, and extends them by learning a metric that efficiently models feature as well as interactions between them. The effectiveness of MLEM is validated through two sets of simulations. First, MLEMs recover ground-truth importance features in synthetic datasets better than state-of-the-art methods, such as Feature Reweighted RSA (FR-RSA). Second, we deploy MLEMs on real language data, where they show stronger robustness to noise in calculating the importance of linguistic features (gender, tense, etc.). MLEMs are applicable to any domains where theoretical features can be identified, such as language, vision, audition, etc. We release optimized code applicable to measure feature importance in the representations of any artificial neural networks or empirical neural data at https://github.com/LouisJalouzot/MLEM.

cs.CL↗

arXiv2025

Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants

We investigate optimal strategies for decoding perceived natural speech from fMRI data acquired from a limited number of participants. Leveraging Lebel et al. (2023)'s dataset of 8 participants, we first demonstrate the effectiveness of training deep neural networks to predict LLM-derived text representations from fMRI activity. Then, in this data regime, we observe that multi-subject training does not improve decoding accuracy compared to single-subject approach. Furthermore, training on similar or different stimuli across subjects has a negligible effect on decoding accuracy. Finally, we find that our decoders better model syntactic than semantic features, and that stories containing sentences with complex syntax or rich semantic content are more challenging to decode. While our results demonstrate the benefits of having extensive data per participant (deep phenotyping), they suggest that leveraging multi-subject for natural speech decoding likely requires deeper phenotyping or a substantially larger cohort.

q-bio.NC↗

arXiv2023

On the (Im)Possibility of Estimating Various Notions of Differential Privacy

We analyze to what extent final users can infer information about the level of protection of their data when the data obfuscation mechanism is a priori unknown to them (the so-called ''black-box'' scenario). In particular, we delve into the investigation of two notions of local differential privacy (LDP), namely ε-LDP and Rényi LDP. On one hand, we prove that, without any assumption on the underlying distributions, it is not possible to have an algorithm able to infer the level of data protection with provable guarantees; this result also holds for the central versions of the two notions of DP considered. On the other hand, we demonstrate that, under reasonable assumptions (namely, Lipschitzness of the involved densities on a closed interval), such guarantees exist and can be achieved by a simple histogram-based estimator. We validate our results experimentally and we note that, on a particularly well-behaved distribution (namely, the Laplace noise), our method gives even better results than expected, in the sense that in practice the number of samples needed to achieve the desired confidence is smaller than the theoretical bound, and the estimation of ε is more precise than predicted.

cs.CR↗