Jianxing Yu

Multi-Hop Question Answering (MHQA) is crucial for evaluating the model’s capability to integrate information from diverse sources. However, creating extensive and high-quality MHQA datasets is challenging: (i) manual annotation is expensive, and (ii) current synthesis methods often produce simpl...

Instruction tuning plays a crucial role in enhancing large language models (LLMs) to better understand complex user instructions. While various data selection and revision methods have been explored to optimize instruction tuning datasets, they face two main challenges: unreasonable pruning of po...

Automatic detection of fraudulent voice calls is essential for online service platforms but faces significant challenges due to the scarcity of labeled data and the continuous evolution of conversational contexts. Standard supervised methods often fail to generalize, as they tend to overfit to va...

This paper focuses on the task of answering complex visual questions that involve cross-dimensional (like 2D to 3D) spatial reasoning. This task (called SpatialQA) can enhance the machine’s spatial cognitive abilities in “plane representation - space reconstruction - semantic inference,” having g...