Papers by Jiaxuan Liu
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety (2024.acl-demos)
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Chuang Liu, Linhao Yu, Jiaxuan Li, Renren Jin, Yufei Huang, Ling Shi, Junhui Zhang, Xinmeng Ji, Tingting Cui, Liutao Liutao, Jinwang Song, Hongying Zan, Sun Li, Deyi Xiong
| Challenge: | a rapid development of Chinese large language models poses big challenges for efficient LLM evaluation. |
| Approach: | They propose an evaluation testbed that benchmarks Chinese LLMs across capability, alignment and safety. |
| Outcome: | The evaluation platform OpenEval benchmarks Chinese LLMs across capability, alignment and safety. |
LLM-Evolve: Evaluation for LLM’s Evolving Capability on Benchmarks (2024.emnlp-main)
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| Challenge: | Existing benchmarks for large language models evaluate LLMs on i.i.d. tasks, overlooking their ability to learn iteratively from past experiences. |
| Approach: | They propose a framework which extends established benchmarks to sequential problem-solving settings and provides feedback after each round to build a demonstration memory that the models can query in future tasks. |
| Outcome: | The proposed framework can improve performance of LLMs by learning from past interactions and improve models' performance over time. |
GUIDE: Towards Scalable Advising for Research Ideas (2026.acl-long)
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| Challenge: | Existing systems that provide detailed, constructive feedback on academic papers struggle with review fidelity. |
| Approach: | They explore factors that underlie the development of robust advising systems . large language models have shown remarkable progress in tasks from text generation to code synthesis . |
| Outcome: | The proposed model outperforms general-purpose language models in acceptance rates for self-ranked top-30% submissions to ICLR 2025. |
Empathy in Diversity: Personalized Depression and Anxiety Therapy via Dialogue State Tracking and Patient-Aware Planning (2026.acl-long)
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Xinwei Yang, Junyi Fan, Yuqing Liu, Jiaxuan Wang, Jiashuai Zhang, Hongru Liang, Wenqiang Lei, Yao Song
| Challenge: | Recent efforts have turned to large language models (LLMs) as therapeutic agents for psychological therapy tasks, yet robustness across diverse patients remains underexplored. |
| Approach: | They propose a realistic role-play protocol for evaluating therapeutic dialogue agents and a de-identified, expert-annotated corpus of therapist–patient dialogues. |
| Outcome: | The proposed framework outperforms baselines on therapeutic outcomes and dialogue quality while improving conversational efficiency. |
Arxiv Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance (2024.emnlp-demo)
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| Challenge: | Existing solutions for document QA fail to provide personalized and up-to-date information efficiently. |
| Approach: | They propose to deploy a self-evolving, efficient LLM system that can offer personalized research services, maintaining a real-time updated database. |
| Outcome: | The proposed system saves 69.92% of time after efficient deployment. |
Multi-Scale Progressive Attention Network for Video Question Answering (2021.acl-short)
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| Challenge: | Experimental evaluations on three benchmarks: TGIF-QA, MSVD-QA and MSRVTT-QA show our method has achieved state-of-the-art performance. |
| Approach: | They propose a multi-scale progressive attention network to fuse visual and text information. |
| Outcome: | The proposed method achieves state-of-the-art on three benchmarks: TGIF-QA, MSVD-QA and MSRVTT-QA. |
DiffStyleTTS: Diffusion-based Hierarchical Prosody Modeling for Text-to-Speech with Diverse and Controllable Styles (2025.coling-main)
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| Challenge: | Existing models for text-to-speech (TTS) synthesize speech with acoustic features . autoregressive models have problems with word skipping and repeated reading . non-autoregressive acustic models lack probabilistic modeling and unimodal characteristics of Gaussian distribution don't conform to true distribution of aural features, which restricts the diversity of generated prosodic features. |
| Approach: | They propose a multi-speaker acoustic model that hierarchically models speech prosodic features and controls different prosodic styles to guide prosody prediction. |
| Outcome: | The proposed method outperforms baseline models in naturalness and achieves superior synthesis speed compared to baseline models. |
Discover and Prove: An Open-source Agentic Framework for Hard Mode Automated Theorem Proving in Lean 4 (2026.acl-long)
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Chengwu Liu, Yichun Yin, Ye Yuan, Jiaxuan Xie, Botao Li, Siqi Li, Jianhao Shen, Yan Xu, Lifeng Shang, Ming Zhang
| Challenge: | Existing approaches to solving mathematical problems fall into two broad categories: informal methods and formal methods. |
| Approach: | They propose to use LLM natural-language reasoning to discover answers . they introduce Discover And Prove framework that rewrites Hard Mode statements into Easy Mode ones for existing ATP provers. |
| Outcome: | The proposed framework can be used to prove hard mode statements on ATP benchmarks. |
SafeScientist: Enhancing AI Scientist Safety for Risk-Aware Scientific Discovery (2025.emnlp-main)
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Kunlun Zhu, Jiaxun Zhang, Ziheng Qi, Nuoxing Shang, Zijia Liu, Peixuan Han, Yue Su, Haofei Yu, Jiaxuan You
| Challenge: | Recent advances in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet raised critical ethical and safety concerns. |
| Approach: | They propose a framework to enhance safety and ethical responsibility in AI-driven scientific exploration. |
| Outcome: | The proposed framework significantly improves safety performance by 35% compared to traditional frameworks. |
CBP-Tuning: Efficient Local Customization for Black-box Large Language Models (2025.emnlp-main)
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| Challenge: | Customized black-box prompt tuning is a new approach to customize large language models . however, as models grow, the resources required for training and deployment become increasingly expensive . |
| Approach: | They propose a framework that facilitates efficient local customization while preserving bidirectional privacy. |
| Outcome: | The proposed framework facilitates efficient local customization while preserving bidirectional privacy. |