Qi Liu

The rapid advancement of large language models (LLMs) has driven the deployment of LLM-based AI tutors on online learning platforms. This widespread adoption highlights an urgent need for systematic benchmarks to evaluate their tutoring capabilities. However, existing evaluations predominantly fo...

Large language models (LLMs) have achieved remarkable performance across diverse tasks, largely driven by large-scale pretraining. However, this data abundance introduces test data contamination, where benchmark datasets overlap with pretraining corpora, undermining the reliability of model evalu...

Retrieval-augmented generation (RAG) effectively extends the knowledge boundaries of large language models (LLMs) for complex tasks, yet current paradigms typically optimize for an interleaving of reasoning and retrieval, where models fail to critically evaluate retrieved information against the...

Large Language Models (LLMs) have become integral to personalized education systems, particularly in the realm of student behavior simulation. By predicting fine-grained learning behaviors, these simulations enable intelligent systems to provide tailored instructional support. However, most exist...

Multimodal Document Question Answering (MDQA) systems commonly follow a retrieve-then-answer paradigm; however, the retrieval stage often introduces substantial noise, making an effective reranking component indispensable.Existing reranker training frameworks in MDQA rely predominantly on proxy s...