Papers by Hui Cai
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing (2022.findings-emnlp)
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Zefeng Cai, Xiangyu Li, Binyuan Hui, Min Yang, Bowen Li, Binhua Li, Zheng Cao, Weijie Li, Fei Huang, Luo Si, Yongbin Li
| Challenge: | Extensive experiments show that STAR outperforms previous pre-training methods and ranks first on the leaderboard . text-to-SQL parsing aims to translate natural language (NL) questions into executable SQL queries . |
| Approach: | They propose a SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing . they propose two objectives that explore context-dependence of NL utterances and SQL queries . |
| Outcome: | The proposed framework outperforms existing methods on two downstream benchmarks and ranks first on the leaderboard. |
Efficient Learned Data Compression via Dual-Stream Feature Decoupling (2026.acl-long)
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| Challenge: | Learned data compression has achieved superior compression ratios, but balancing precise probability modeling with system efficiency remains challenging. |
| Approach: | They propose a Dual-Stream Multi-Scale Decoupler that disentangles local and global contexts to replace deep serial processing with shallow parallel streams. |
| Outcome: | The proposed method achieves state-of-the-art performance in both compression ratio and throughput while maintaining the lowest latency and memory usage. |
AgentGC: Evolutionary Learning-based Lossless Compression for Genomics Data with LLM-driven Multiple Agent (2026.findings-acl)
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Sun Hui, Ding Yanfeng, Huidong Ma, Chang Xu, Keyan Jin, Lizheng Zu, Cheng Zhong, Xiaoguang Liu, Gang Wang, Wentong Cai
| Challenge: | Lossless compression has made significant advancements in Genomics Data storage, sharing and management. |
| Approach: | They propose a novel agent-based GD Compressor with 3 layers with a multi-agent named Leader and Worker. |
| Outcome: | The proposed method improves on existing methods with low-level modeling and limited adaptability and user-unfriendly interface. |
Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning (2024.findings-naacl)
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| Challenge: | Existing approaches to enable large language models to implement function calling are limited in their tool-use capabilities. |
| Approach: | They propose a controllable, target-driven approach to empower LLMs to operate external APIs only via prompts. |
| Outcome: | The proposed approach limits LLMs to executing simple tasks, e.g., API Selection and Argument Completion. |
AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation (2026.findings-acl)
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Wentao Shi, Yu Wang, Yuyang Zhao, Yuxin Chen, Fuli Feng, Xueyuan Hao, Xi Su, Qi GU, Hui Su, Xunliang Cai, Xiangnan He
| Challenge: | Existing approaches to verify agent behaviors in complex environments rely on rule-based verifiers or LLM-as-a-Judge models. |
| Approach: | They propose a benchmark to evaluate Agent-as-a-Judge across three domains . the benchmark covers search, data systems, and graphical user interfaces - with 155 tasks and 516 trajectories . |
| Outcome: | The proposed benchmark outperforms existing benchmarks in search, data systems, and GUI domains while revealing open challenges in agent-based verification. |
Efficient Knowledge Infusion via KG-LLM Alignment (2024.findings-acl)
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| Challenge: | Existing methods for knowledge infusion face knowledge mismatch and poor information compliance of LLMs with knowledge graphs. |
| Approach: | They propose a three-stage alignment strategy to enhance the LLM's capability to utilize information from knowledge graphs. |
| Outcome: | The proposed method outperforms baselines on biomedical question-answering datasets and outperformed existing methods. |
Flexibly Utilize Memory for Long-Term Conversation via a Fragment-then-Compose Framework (2025.emnlp-main)
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Cai Ke, Yiming Du, Bin Liang, Yifan Xiang, Lin Gui, Zhongyang Li, Baojun Wang, Yue Yu, Hui Wang, Kam-Fai Wong, Ruifeng Xu
| Challenge: | Large language models extract useful information from conversation history to enhance the response in long-term conversations. |
| Approach: | They propose a Fragment-then-Compose framework to optimize memory utilization for long-term open-domain conversation. |
| Outcome: | The proposed framework can be used to extract useful information from conversation history . it can be adapted to different situations and improve response generation . |
Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition (2022.naacl-main)
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| Challenge: | a novel AI-empowered chat bot for learning as conversation can be applied to various domains without in-domain dialogue data. |
| Approach: | They propose a novel task where a user does not read a passage but gains information and knowledge through conversation with a teacher bot. |
| Outcome: | The proposed system can be transferred to various domains without in-domain dialogue data and can carry out conversations both informative and attentive to users. |