Papers by Boyan Shi
Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL (2025.naacl-long)
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| Challenge: | Existing approaches to generative language models struggle to handle the increasing complexity of multi-turn Text-to-SQL tasks. |
| Approach: | They propose a framework which enhances generative language models with dual-extractive modules designed to track schema and contextual changes in multi-turn Text-to-SQL. |
| Outcome: | The proposed framework achieves state-of-the-art performance on SparC and CoSQL datasets and significantly improves execution accuracy in multi-turn interactions by 7.1% and 9.55%. |
STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation (2026.acl-long)
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Shuyuan Zhao, Wei Chen, Weijie Zhang, Xinrui Hou, Junfeng Shen, Boyan Shi, Shengnan Guo, Youfang Lin, Huaiyu Wan
| Challenge: | Temporal Knowledge Graphs (TKGs) store dynamic facts in the real world. |
| Approach: | They propose a Spatial-Temporal Knowledge Adapter which integrates the evolving graph encoder and the LLM to facilitate TKG reasoning. |
| Outcome: | The proposed method outperforms state-of-the-art methods on benchmark datasets and exhibits strong generalization capabilities in cross-dataset task. |
SAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive Learning (2026.findings-acl)
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| Challenge: | Existing methods for multitask learning fail to match input semantics with expert capabilities, leading to weak expert specialization. |
| Approach: | They propose a parameter-efficient mixture-of-experts framework for task-adaptive learning that aligns textual semantics with the most suitable experts for precise routing. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods and holds excellent task generalization capabilities. |
MMErroR: A Benchmark for Erroneous Reasoning in Vision-Language Models (2026.acl-long)
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Yang Shi, Yifeng Xie, Minzhe Guo, Liangsi Lu, Mingxuan Huang, Jingchao Wang, Zhihong Zhu, Boyan Xu, Zhiqi Huang
| Challenge: | Recent advances in vision-language models have improved performance in multi-modal learning. |
| Approach: | They propose a multi-modal benchmark that embeds a single coherent reasoning error in 1997 samples. |
| Outcome: | The proposed benchmark is based on a set of 1997 samples embedding a single coherent reasoning error. |
ROSE: An Intent-Centered Evaluation Metric for NL2SQL (2026.acl-long)
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| Challenge: | Existing evaluation metrics for evaluating the effectiveness of Natural Language to SQL (NL2SQL) solutions are becoming unreliable due to its sensitiveness to syntactic variation and inconsistent consistency with ground-truth SQL. |
| Approach: | They propose an intent-centered metric that focuses on whether the predicted SQL answers the question, rather than consistency with the ground-truth SQL. |
| Outcome: | The proposed metric outperforms the next-best metric by nearly 24% on the expert-aligned validation set **ROSE-VEC**. |
GenLink: Generation-Driven Schema-Linking via Multi-Model Learning for Text-to-SQL (2025.emnlp-main)
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| Challenge: | Experimental results on BIRD and Spider benchmarks validate the effectiveness of GenLink. |
| Approach: | They propose a generation-driven schema-linking framework based on multi-model learning . experimental results validate the effectiveness of GenLink . |
| Outcome: | Experimental results show that GenLink improves schema-linking recall rate and cross-domain adaptability. |