most citedWHERE and WHICH: Iterative Debate for Biomedical Synthetic Data Augmentation

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cs.CL2026

Beyond the Literal: Decomposing Pragmatic Intent in Multimodal Meme Understanding

Zhengyi Zhao, Shubo Zhang, Zezhong Wang +6

When asked what a meme or sarcastic post means, Large Vision Language Models (LVLMs) tend to describe what the image shows rather than what the author is trying to communicate. Sta…

cs.CL2026

Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation

Zhengyi Zhao, Shubo Zhang, Zezhong Wang +7

Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs) by providing access to external knowledge. However, current research primarily focuses on r…

cs.CL2025

Dual-Density Inference for Efficient Language Model Reasoning

Zhengyi Zhao, Shubo Zhang, Yuxi Zhang +3

Large Language Models (LLMs) have shown impressive capabilities in complex reasoning tasks. However, current approaches employ uniform language density for both intermediate reason…

cs.CL2025

T: An Adaptive Test-Time Scaling Strategy for Contextual Question Answering

Zhengyi Zhao, Shubo Zhang, Zezhong Wang +7

Recent advances in Large Language Models (LLMs) have demonstrated remarkable performance in Contextual Question Answering (CQA). However, prior approaches typically employ elaborat…

cs.CL2025★ 2 cited

WHERE and WHICH: Iterative Debate for Biomedical Synthetic Data Augmentation

Zhengyi Zhao, Shubo Zhang, Bin Liang +2

In Biomedical Natural Language Processing (BioNLP) tasks, such as Relation Extraction, Named Entity Recognition, and Text Classification, the scarcity of high-quality data remains…

cs.CL2025

FReM: A Flexible Reasoning Mechanism for Balancing Quick and Slow Thinking in Long-Context Question Answering

Zhengyi Zhao, Shubo Zhang, Zezhong Wang +3

Long-context question-answering (LCQA) systems have greatly benefited from the powerful reasoning capabilities of large language models (LLMs), which can be categorized into slow a…

cs.CL2025

EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems

Zhengyi Zhao, Shubo Zhang, Yiming Du +5

Large language models have improved dialogue systems, but often process conversational turns in isolation, overlooking the event structures that guide natural interactions. Hence w…