Papers by Jionglong Su
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation (2025.acl-long)
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Haochen Xue, Feilong Tang, Ming Hu, Yexin Liu, Qidong Huang, Yulong Li, Chengzhi Liu, Zhongxing Xu, Chong Zhang, Chun-Mei Feng, Yutong Xie, Imran Razzak, Zongyuan Ge, Jionglong Su, Junjun He, Yu Qiao
| Challenge: | Existing multimodal large language models lack the ability to memorize, recall, and reason in sustained interactions. |
| Approach: | They propose a multimodal real-world conversation benchmark for evaluating open-ended abilities of multimodal large language models. |
| Outcome: | The proposed benchmarks show that the models perform better in open-ended conversations. |
FinBPM: A Framework for Portfolio Management-based Financial Investor Behavior Perception Model (2024.eacl-long)
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| Challenge: | a portfolio management framework based on reinforcement learning is needed to optimize stock price movements. |
| Approach: | They propose a framework that takes irrational investment into account when calculating portfolio weights . they use financial text to analyze intrinsic value information of companies and time series data . |
| Outcome: | The proposed framework gains 13.26% returns over state-of-the-art models while controlling for risk. |
News2vec: News Network Embedding with Subnode Information (D19-1)
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| Challenge: | Existing approaches to embed news as vectors do not integrate features and inter-textual knowledge of news. |
| Approach: | They propose a model that integrates news features and inter-textual knowledge into a dense vector representation. |
| Outcome: | The proposed model can be used to represent news as a dense vector . it is compared with existing models on stock movement prediction and news recommendation tasks . |
MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses (2025.emnlp-main)
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Tong Chen, Zimu Wang, Yiyi Miao, Haoran Luo, Sun Yuanfei, Wei Wang, Zhengyong Jiang, Procheta Sen, Jionglong Su
| Challenge: | Existing medical fact-checking datasets focus on human-generated content, leaving the verification of content generated by large language models (LLMs) relatively unexplored. |
| Approach: | They propose to use Chinese medical fact-checking datasets to verify LLM-generated medical content by combining in-context learning and fine-tuning. |
| Outcome: | The first evidence-based Chinese medical fact-checking dataset of LLM-generated medical content consists of 1,321 questions and 7,409 claims . |
Dialectic-Med: Mitigating Diagnostic Hallucinations via Counterfactual Adversarial Multi-Agent Debate (2026.findings-acl)
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| Challenge: | Existing Chain-of-Thought (CoT) approaches lack intrinsic correction mechanisms, rendering them vulnerable to error propagation. |
| Approach: | They propose a multi-agent framework that enforces diagnostic rigor through adversarial dialectics. |
| Outcome: | Empirical evaluations show that the proposed framework improves explanation faithfulness and mitigates hallucinations. |