Orateur
Description
Title: Knowledge Boundary Awareness in Large Language Models
Abstract: Large language models (LLMs) possess remarkable knowledge and reasoning capabilities, yet their capabilities are bounded. A fundamental challenge for trustworthy and efficient AI is enabling models to identify the limits of their own knowledge before producing an answer. In this talk, I will present our recent work on knowledge boundary awareness, a generation-free framework for estimating whether an LLM can answer a query. I will then discuss how knowledge boundary awareness helps build more efficient AI systems by reducing inference cost while maintaining the original performance.
Bio: Lihu Chen is a Research Associate at Imperial College London. He received his Ph.D. from Télécom Paris (Institut Polytechnique de Paris) and previously held a postdoctoral position at Inria Saclay. His research focuses on natural language processing and large language models, with interests in trustworthy and efficient AI, information extraction, and biomedical NLP. He develops open-source models and tools to improve the reliability and efficiency of AI systems.