Few-shot and zero-shot models for Information Extraction

Job description: Information Extraction aims to identify concepts or facts in texts and to structure the information. In this field, a major challenge is to design high-performance models using only few annotated data (few-shot), or even no annotated data at all (zero-shot). The proposed topic for this PhD falls within this framework, and will focus in particular on exploiting the capabilities of large pre-trained language models (LLMs) for this task. More specifically, the avenues explored could cover approaches for large models distillation in order to produce training data for information extraction, a study of possible synergies between large-scale model pre-training and episodic meta-learning, or the proposal of new methods for building pre-training data, using for example distant supervision from structured knowledge bases.

Your profile: Master 2 en informatique ou diplôme dapos;ingénieur informatique