91视频-全网首发91视频私密资源站

报告时间：待定

报告地点：待定

报告专家：聂建云 加拿大蒙特利尔大学的教授

报告内容：Large language models (LLMs) augmented with knowledge graphs (KGs) offer a promising approach for knowledge-intensive reasoning. Central to this approach is the selection of appropriate reasoning paths in the KG. Yet, existing methods face a common limitation: reasoning path selection is often performed by separate modules using criteria that are only weakly connected to the reasoning requirements. This often results in selecting incorrect relations or premature pruning of relevant paths. We propose Search-on-Graph (SoG), a method that strengthens the connection between path selection and reasoning by having the LLM itself select which relations to follow, informed by both the available KG structure and the complete reasoning history. SoG follows an observe-think-navigate paradigm: at each step, the LLM observes the relational connections available at the current entity, reasons about which path best advances toward answering the question, and navigates accordingly. This context-aware navigation fully exploits the LLM’s reasoning capabilities rather than relying on independent selection modules with surrogate criteria. Experiments on six knowledge graph question answering (KGQA) benchmarks demonstrate that SoG outperforms state-of-the-art methods while requiring no task-specific fine-tuning and generalizing across different KG schemas.

专家简介：

​

Bio

聂建云是加拿大蒙特利尔大学的教授。他长期从事信息检索，自然语言处理和人工智能方面的研究，他的研究在这些领域里产生了较大的影响。2022年被ACM SIGIR学会选为院士（member of SIGIR academy）。聂建云也是加拿大自然语言信息处理和应用方向的讲座教授（Canada Research Chair）。