Stage Steering Llm To Inhibit Biases- Saclay H/F - CEA

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- La conscience des responsabilités
- La coopération
- La curiosité

As an intern at the CEA, you will have the opportunity to work in a world-renowned research environment. Our teams consist of passionate and dedicated experts, providing an environment conducive to learning and collaboration. You will have access to state-of-the-art equipment and top-tier research resources to carry out your assignments. The work performed may potentially lead to a scientific publication.

Context

Through the thesis of Clément Cornet, the team has already developped several approaches of steering and other works in mechanistic interpretability [1,2]. A large part of the work is integrated into a light python library that can serve as basis for the work.

What do we expect from you ?

The intern will work on the following tasks :

• Conduct a literature review on methods for bias in inhibition in multimodal LLMs with steering
• Conduct experiments with available steering approaches to inhibate biases, including a rigorous quantitative evaluation on well chosen models and modalities
• Develop novel approaches to inhibate biases with steering, in particular to determine its strength automatically
• Develop a demonstrator to showcase the work carried out

Depending on the profile and motivation of the intern, the work may lead to a scientific publication and may be pursued with a PhD focused on a similar topic. The person will work in collaboration with Clement Cornet, Hervé Le Borgne, Romaric Besançon and possibly other researchers of the lab, depending on the direction of the work.

[1] Cornet et al (2025) Explaining How Visual, Textual and Multimodal Encoders Share Concepts, CoRR:2507.18512
[2] Cornet et al (2026) The Deleuzian Representation Hypothesis, ICLR

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Profil:

• Students in their final year of studies (M2 or last year of engineering school)
• Strong foundations in machine learning and deep learning
• Interest in mechanistic interpretability and bias of AI models
• Python proficiency in pytorch