Papers by Yiqun Sun
STICKERCONV: Generating Multimodal Empathetic Responses from Scratch (2024.acl-long)
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Yiqun Zhang, Fanheng Kong, Peidong Wang, Shuang Sun, SWangLing SWangLing, Shi Feng, Daling Wang, Yifei Zhang, Kaisong Song
| Challenge: | Prior studies on stickers focused on sentiment analysis and recommendation systems, overlooking their vast potential in empathetic response generation. |
| Approach: | They propose a multimodal empathetic dialogue dataset, STICKERCONV, which simulates human behavior with stickers, and propose evaluative metrics based on LLM. |
| Outcome: | The proposed framework generates contextually relevant and emotionally resonant multimodal empathetic responses, contributing to the advancement of more nuanced and engaging e-dialog systems. |
Don’t Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space Transformation (2025.acl-long)
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| Challenge: | Existing methods for text embedding require re-encoding the entire corpus for each instruction. |
| Approach: | They propose a framework that generates dynamic text embeddings that adapt to user instructions, highlighting specific attributes of text. |
| Outcome: | The proposed framework improves instruction-following text embedding quality over state-of-the-art methods while speeding up processing on large datasets. |
If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs (2026.acl-long)
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Siqi Fan, Xiusheng Huang, Yiqun Yao, Xuezhi Fang, Kang Liu, Peng Han, Shuo Shang, Aixin Sun, Yequan Wang
| Challenge: | Existing benchmarks for large language models (LLMs) fail to capture these dynamics, focusing on static, open-ended evaluations. |
| Approach: | They propose a benchmark to assess lifelong learning in large language models . they use two episodic datasets rich in narrative structure and character interactions . |
| Outcome: | Experiments on LLMs show that non-parametric methods outperform parametric ones in managing stateful learning. |
PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder (2025.acl-long)
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| Challenge: | Existing embedding models excel at capturing general meaning, but overlook ideological nuances, limiting their effectiveness in political bias tasks. |
| Approach: | They propose a framework to Produce inteRpretable polItical biaS eMbeddings. |
| Outcome: | The proposed framework outperforms state-of-the-art embedding models in political bias classification . the proposed framework offers highly interpretable representations for political analysis . |
Uncovering the Bigger Picture: Comprehensive Event Understanding Via Diverse News Retrieval (2025.emnlp-main)
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| Challenge: | Existing news retrieval systems prioritize textual relevance, leading to redundancy and limited viewpoint exposure. |
| Approach: | They propose a framework that explicitly models semantic variation at the sentence level. |
| Outcome: | The proposed framework outperforms strong baselines in terms of retrieval diversity without compromising relevance. |