Papers by Jiaxing Sun
OpenHuEval: Evaluating Large Language Model on Hungarian Specifics (2025.findings-acl)
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Haote Yang, Xingjian Wei, Jiang Wu, Noémi Ligeti-Nagy, Jiaxing Sun, Yinfan Wang, Győző Zijian Yang, Junyuan Gao, Jingchao Wang, Bowen Jiang, Shasha Wang, Nanjun Yu, Zihao Zhang, Shixin Hong, Hongwei Liu, Wei Li, Songyang Zhang, Dahua Lin, Lijun Wu, Gábor Prószéky, Conghui He
| Challenge: | Recent advances in Large Language Models (LLMs) represent significant strides toward artificial general intelligence (AGI). |
| Approach: | They introduce OpenHuEval, the first benchmark for LLMs focusing on the Hungarian language and specifics. |
| Outcome: | The framework reveals intrinsic patterns and mechanisms of LLMs in non-English languages, with Hungarian serving as an example. |
CAPE: A Chinese Dataset for Appraisal-based Emotional Generation in Large Language Models (2025.findings-naacl)
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| Challenge: | Existing LLMs fail to capture the nuances of human emotions, making their interactions seem impersonal or inadequate. |
| Approach: | They propose a two-stage automatic data generation framework to generate a Chinese dataset called CAPE . their data is a cognitive appraisal theory-based Emotional corpus that accounts for personal and situational factors. |
| Outcome: | The proposed framework can generate human-like responses in conversation with large language models. |
Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations (2024.acl-long)
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| Challenge: | Currently, many benchmarks evaluate the commonsense reasoning of large language models (LLMs), but most are English-based, limiting non-English evaluations. |
| Approach: | They propose to use Chinese commonsense reasoning to evaluate LLMs' commonsensing ability. |
| Outcome: | The proposed benchmark covers both globally known and Chinese-specific commonsense reasoning abilities and can be used as a reference for future research. |
Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process (2024.lrec-main)
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| Challenge: | Existing studies have not explored the link between PLMs’ pre-training-based knowledge and input passages. |
| Approach: | They propose a framework that uses prompts to connect explicit and implicit knowledge to elicit type-specific reasoning via prompts, a form of implicit knowledge. |
| Outcome: | The proposed model performs comparable to the state-of-the-art on HotpotQA. |
ModularMoE: Fast LLM Customization with Parameter-Sharing Mixture-of-Experts for Low-Resource Settings (2026.findings-acl)
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Jiaxing Liu, Qi Qi, Haifeng Sun, Dunjun Li, Zirui Zhuang, Bo He, Xiang Yang, Cong Liu, Jianxin Liao, Jingyu Wang
| Challenge: | Large Language Models impose significant computational and storage burdens on personal devices . existing customization approaches incur excessive computational costs or lead to suboptimal performance . |
| Approach: | They propose a training framework that converts pre-trained LLMs into parameter-sharing MoE models for lightweight deployment. |
| Outcome: | The proposed training framework outperforms state-of-the-art training frameworks at the same sparsity level while delivering up to 2.71 inference speedup. |