Papers by Lixing Lin
NarrativePlay: Interactive Narrative Understanding (2024.eacl-demo)
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| Challenge: | Existing systems for interactive agents focus on specific capabilities in predetermined scenarios. |
| Approach: | They propose a novel system that allows users to role-play a fictional character and interact with other characters in narratives in an immersive environment. |
| Outcome: | The proposed system generates human-like responses guided by personality traits extracted from narratives. |
SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator (2026.acl-long)
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Xueyang Zhou, Weidong Wang, Lin Lu, Jiawen Shi, Guiyao Tie, Xu Yongtian, Lixing Chen, Pan Zhou, Neil Zhenqiang Gong, Lichao Sun
| Challenge: | SafeAgent improves agent safety through fully automated synthetic data generation. |
| Approach: | They propose a framework that improves agent safety through fully automated synthetic data generation. |
| Outcome: | The proposed framework outperforms closed-source models on two safety benchmarks and one real-world task. |
Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection (2021.acl-long)
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| Challenge: | Emotion detection in dialogues requires the identification of thematic topics underlying a conversation, commonsense knowledge, and the intricate transition patterns between affective states. |
| Approach: | They propose a Topic-Driven Knowledge-Aware Transformer model that integrates topic representation and commonsense knowledge from ATOMIC for dialogue emotion detection. |
| Outcome: | The proposed model outperforms state-of-the-art models on four dialogue datasets . it can detect topics which help distinguish emotion categories, the authors show . |
Identifying Cellular Niches in Spatial Transcriptomics: An Investigation into the Capabilities of Large Language Models (2025.acl-long)
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Huanhuan Wei, Xiao Luo, Hongyi Yu, Jinping Liang, Luning Yang, Lixing Lin, Alexandra Popa, Xiting Yan
| Challenge: | Spatial transcriptomic technologies allow measuring gene expression profile and spatial information of cells in tissues simultaneously. |
| Approach: | They propose a spatial transcriptomic approach to identify spatial niches using a zero-shot large language models by transforming spatial transcriptomics data into spatial context prompts. |
| Outcome: | The proposed model improves performance by leveraging gene expression of neighboring cells/spots, cell type composition, tissue information, and external knowledge. |
Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) excel in generating coherent texts, but their ability to comprehend the author’s thoughts remains uncertain. |
| Approach: | They conduct a comprehensive survey of narrative understanding tasks, examining their key features, definitions, taxonomy, associated datasets, evaluation metrics, and limitations. |
| Outcome: | The proposed framework could be extended to address novel narrative understanding tasks. |
PANACEA: An Automated Misinformation Detection System on COVID-19 (2023.eacl-demo)
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Runcong Zhao, Miguel Arana-catania, Lixing Zhu, Elena Kochkina, Lin Gui, Arkaitz Zubiaga, Rob Procter, Maria Liakata, Yulan He
| Challenge: | Using social media and fact-checking to detect misinformation is not enough to prevent the spread of false information. |
| Approach: | They propose a web-based misinformation detection system PANACEA which has two modules, fact-checking and rumour detection. |
| Outcome: | The system outperforms state-of-the-art methods and adapts graph convolutional networks model to detect rumours based on tweets rather than knowledge bases. |