Formalismes de Représentation Sémantique et Raisonnement à l'ère des Grands Modèles de Langage

Large Language Models (LLMs) are now used for a wide range of tasks, many of which require reasoning abilities. However, these abilities remain limited and lack transparency. This thesis explores how to improve the reasoning, transparency and robustness of LLMs by integrating symbolic structures.First, we conduct an analysis of the societal issues arising from the new role of LLMs in our access to knowledge. Fifteen major issues are identified, as well as current and potential mitigation strategies, drawing on both technical solutions and regulatory approaches.The thesis then focuses on Meaning Representation Frameworks (MRFs), which encode the semantics of natural language into graph structures. A comprehensive survey of MRFs is presented, introducing a new classification based on their structural properties, as well as the available resources, empirical use and research directions. This repositions MRFs as computational artifacts capable of complementing neural models in complex tasks.Building upon this, the thesis introduces VANESSA, a neuro-symbolic reasoning system that integrates MRFs with LLMs, as well as new representation and symbolic parsing process. VANESSA uses this representation to decompose reasoning problems into three simpler subtasks: parsing, natural language inference (NLI) and formal solving. Experimental results show that VANESSA achieves performance comparable to LLMs on logical reasoning tasks, while producing outputs that are traceable and explainable, illustrating the added value of hybrid architectures.Finally, the thesis addresses the problem of step-by-step verification of reasoning chains, which are produced by LLMs. A novel benchmark of nearly 5,000 annotated reasoning steps is presented, assessing both logical validity and factual correctness. Though LLMs are able to detect some errors, neuro-symbolic approaches such as VANESSA achieve comparable performance while providing valuable transparency.Overall, the thesis advocates a hybrid vision of language-based artificial intelligence, where LLMs and symbolic structures are not competing paradigms but complementary tools. It opens new perspectives towards AI systems that are not only powerful, but also responsible, trustworthy and interpretable, combining the flexibility of neural models with the rigor of symbolic reasoning.