Zhijing Wu

Chain-of-Thought (CoT) reasoning is crucial for the performance of Large Reasoning Models (LRMs) but is often hindered by redundant and distracting segments, which incur excessive inference costs and degrade robustness. Existing approaches try to solve this problem by enforcing brevity through ex...

Large Language Models (LLMs) have shown great potential in Knowledge Base Question Answering (KBQA) via semantic parsing. However, existing retrieval-augmented approaches typically retrieve entities and relations in isolation based solely on semantic similarity, ignoring the structural informatio...

Knowledge within large language models (LLMs) inevitably lags behind an evolving world, motivating knowledge editing methods that update facts without expensive retraining. In multi-hop knowledge editing, models must not only recall updated facts but also correctly propagate them through multi-st...

User satisfaction serves as a key indicator of search experience in Generative Information Retrieval (GenIR) systems, and its accurate estimation is essential for system optimization. Extensive research in traditional IR has established interaction signals (e.g., dwell time, clicks, query reformu...