Guodong Zhou

Retrieval-Augmented Generation (RAG) grounds language models in external evidence, but multi-hop question answering remains difficult because iterative pipelines must control what to retrieve next and when the available evidence is adequate. In practice, systems may answer from incomplete evidenc...

The semantic gap between colloquial user queries and professional legal documents presents a fundamental challenge in Legal Case Retrieval (LCR). Existing dense retrieval methods typically treat LCR as a black-box semantic matching process, neglecting the explicit juridical logic that underpins l...

In ancient China, a variety of datasets depicted humanistic scenes, geographical features, and plants. However, these datasets, compiled long ago, often contain errors, lack comprehensiveness, and are inconsistent with modern realities. To meet current demands, we aim to expand and improve ancien...

Aspect-based sentiment analysis has garnered increasing attention in the research community; however, most studies have predominantly focused on English datasets, with other languages such as Chinese, Japanese, and German being neglected due to the limited availability of adequately labeled data....

Dangerous speech detection is a well-studied task, but existing approaches typically treat utterances in isolation, relying on binary labels that ignore who is speaking and in what mental state. We formulate a context-dependent variant of this task by grounding it in Theory-of-Mind (ToM). In cogn...

Long-form audio meeting understanding (LAMU) is gaining attention, but dedicated question answering (QA) datasets are lacking. Previous tailored speech QA and existing Speech LLMs suffer from acoustic information loss and poor long-term dependency capture. We construct the LongAudioQA dataset and...