Рет қаралды 727
Abstract: How to best represent words, documents, queries, entities, relations, and other variables in information retrieval (IR) and related applications has been a fundamental research question for decades. Early IR systems relied on the independence assumptions about words and documents for simplicity and scalability, which were clearly sub-optimal from a semantic point of view. The rapid development of deep neural networks in the past decade has revolutionized the representation learning technologies for contextualized word embedding and graph-enhanced document embedding, leading to the new era of dense IR. This talk highlights such impactful shifts in representation learning for IR and related areas, the new challenges coming along and the remedies, including our recent work in large-scale dense IR, in graph-based reasoning for knowledge-enhanced predictions, in self-refinement of large language models (LLMs) with retrieval augmented generation (RAG) and iterative feedback, in principle-driven selfalignment of LLMs with minimum human supervision, etc. More generally, the power of such deep learning goes beyond IR enhancements, e.g., for significantly improving the state-of-the-art solvers for NP-Complete problems in classical computer science.
Bio: Yiming Yang is a professor with a joint appointment at the Language Technologies Institute (LTI) and the Machine Learning Department (MLD) in the School of Computer Science, Carnegie Mellon University (CMU). She has jointed CMU as a faculty member since 1996, and her research has been focused on machine learning paradigms, algorithms and applications in a broad range, including her influential early work in large-scale text classification and information retrieval, and more recently on cutting-edge technologies for large language models (e.g., XL-Net), neural-network architecture search (e.g., DARTS), reasoning with graph neural networks, reinforcement learning and diffusion models for solving NP complete problems (e.g., DIMES and DIFFUSCO), AI-enhanced self-alignment of LLMs, knowledge-enhanced information retrieval, LLMs with RAG (Retrieval Augmented Generation), large foundation models for scientific domains, etc. She became a member of the SIGIR Academy in 2023, in recognition for her contributions in the intersection of Machine Learning and Information Retrieval.