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

2 citations · 2 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL2025

Memory-T1: Reinforcement Learning for Temporal Reasoning in Multi-session Agents

Yiming Du, Baojun Wang, Yifan Xiang +11

Temporal reasoning over long, multi-session dialogues is a critical capability for conversational agents. However, existing works and our pilot study have shown that as dialogue hi…

cs.CL2025

ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning

Yiming Du, Yifan Xiang, Bin Liang +3

Fine-tuning multi-turn dialogue systems requires high-quality supervision but often suffers from degraded performance when exposed to low-quality data. Supervision errors in early…

cs.AI2025

MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models

Zhengyi Zhao, Shubo Zhang, Yuxi Zhang +10

Memes have emerged as a popular form of multimodal online communication, where their interpretation heavily depends on the specific context in which they appear. Current approaches…

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.CL20252 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…