General information
Organisation
The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas :
• defence and security,
• nuclear energy (fission and fusion),
• technological research for industry,
• fundamental research in the physical sciences and life sciences.
Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.
The CEA is established in ten centers spread throughout France
Reference
2026-41862
Description de l'unité
Based in Saclay (Essonne), the LIST is one of the two institutes of CEA Tech, the Technological Research Division of the CEA. Dedicated to intelligent digital systems, its mission is to carry out technological developments of excellence on behalf of industrial partners, in order to create value.
Within the LIST, the Laboratory of Textual and Visual Semantic Analysis (LASTI) conducts its research in the field of natural language processing and computer vision to extract, classify and generate information. The laboratory's research themes include learning with few data, trustworthiness and multimodality.
Position description
Category
Engineering science
Contract
Internship
Job title
Stage Steering LLM to inhibit biases- Saclay-H/F
Subject
Multimodal Large Language Models (LLMs) are increasingly capable of processing and integrating information from multiple modalities, including text, images, audio, and speech. However, these models can inherit and amplify biases across modalities, potentially affecting the fairness, reliability, and robustness of their outputs. This internship will investigate steering-based approaches to identify and inhibit such biases at the model level, with the goal of developing more controllable and robust multimodal LLMs.
Contract duration (months)
6 mois
Job description
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
#Cea List
Methods / Means
Python - PyTorch
Applicant Profile
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
Position location
Site
Saclay
Job location
France, Ile-de-France, Essonne (91)
Location
Saclay
Candidate criteria
Prepared diploma
Bac+5 - Master 2
Requester
Position start date
01/12/2026
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