Papers by Chuanyi Liu
AFT-Tab: Adversarial Fine-Tuning for Tabular Data Synthesis with Long Text Columns (2026.acl-long)
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| Challenge: | Existing tabular data synthesis methods fail to account for cross-modal heterogeneity of real-world tables, where structured continuous and discrete attributes coexist with unstructured long-text columns. |
| Approach: | They propose a framework that synergistically trains an LLM-based text generator and a deep-learning-based non-textual generator to quantify cross-modal semantic alignment. |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks in fidelity, diversity, and task utility. |
SGPVT: Self-Generated Proximal Visual Tokens for Mitigating Proximal Collateral Damage in MLLM Unlearning (2026.acl-long)
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| Challenge: | Existing approaches focus on general utility metrics, overlooking the preservation of semantically related concepts. |
| Approach: | They propose a method that introduces self-generated proximal visual tokens to prevent forgetting vulnerability. |
| Outcome: | The proposed framework outperforms existing methods in preserving semantically related concepts while achieving effective target unlearning. |
Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning (2026.findings-acl)
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| Challenge: | Existing methods for reinforcement learning with verifiable rewards (RLVR) rely on static objective functions and rigid clipping strategies that misalign with the model’s evolving reasoning capabilities. |
| Approach: | They propose to incorporate Power-Mean Policy Optimization (PMPO) and Feedback-Adaptive Clipping (FAC) to overcome limitations of static mechanisms. |
| Outcome: | Extensive experiments on nine reasoning tasks show the proposed paradigm outperforms state-of-the-art methods. |
Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs (2026.acl-long)
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| Challenge: | Existing unsupervised reinforcement learning methods lack the capacity to adapt to the model’s evolving reasoning capabilities during training. |
| Approach: | They propose an unsupervised reinforcement learning algorithm that adapts rewards to balance consensus and exploration based on the Free Energy Principle. |
| Outcome: | Empirical evaluations on nine datasets show that FREIA outperforms baseline methods on reasoning tasks. |
TS-SQL: Test-driven Self-refinement for Text-to-SQL (2025.findings-emnlp)
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| Challenge: | null |
| Approach: | null |
| Outcome: | null |
Enhancing Text-to-SQL Parsing through Question Rewriting and Execution-Guided Refinement (2024.findings-acl)
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| Challenge: | Existing prompt engineering methods exploit database content and execution feedback to improve text-to-sql performance. |
| Approach: | They propose a framework for large language model-based text-to-sql task that exploits database content and execution feedback to improve execution accuracy. |
| Outcome: | The proposed framework improves execution accuracy and usability by 12.41% and 5.38% on four widely used benchmarks. |
Chain-of-Talkers (CoTalk): Fast Human Annotation of Dense Image Captions (2025.emnlp-main)
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| Challenge: | Existing approaches for optimizing human annotation efforts are limited . et al., 2015) suggest that densely annotated image captions improve vision-language alignment . |
| Approach: | They propose an AI-in-the-loop methodology to maximize the number of annotated samples and improve their comprehensiveness under fixed budget constraints. |
| Outcome: | The proposed method improves annotation speed and retrieval performance over the parallel method. |
DSQG-Syn: Synthesizing High-quality Data for Text-to-SQL Parsing by Domain Specific Question Generation (2025.findings-naacl)
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Shaoming Duan, Youxuan Wu, Chuanyi Liu, Yuhao Zhang, Zirui Wang, Peiyi Han, Shengyuan Yu, Liang Yan, Yingwei Liang
| Challenge: | Existing methods for generating SQL queries using natural language questions produce inconsistent NLQ-SQL pairs. |
| Approach: | They propose a text-to-SQL data synthesis framework that generates domain-relevant questions . they synthesize NLQ-SqL pairs that are domain-specific and intent-consistent . |
| Outcome: | The proposed method outperforms closed-source LLMs on the Text-to-SQL task. |
Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling (2023.emnlp-main)
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| Challenge: | Recent studies suggest that transformer-based models perform cross-attention over input pairs, leading to computational cost. |
| Approach: | They propose a lightweight cross-attention mechanism that performs query encoding only once while modeling the query-candidate interaction in parallel. |
| Outcome: | The proposed model speeds up sentence pairing by over 113x while achieving comparable performance as the more expensive models. |
SPFT-SQL: Enhancing Large Language Model for Text-to-SQL Parsing by Self-Play Fine-Tuning (2025.findings-emnlp)
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| Challenge: | Existing methods for self-play fine-tuning do not generate new information and the large number of correct SQL queries produced by the opponent model reduces the main model’s ability to generate accurate SQL queries. |
| Approach: | They propose a self-play fine-tuning method tailored for the Text-to-SQL task that synthesizes high-quality fine- tuning data iteratively based on the database schema and validation feedback to enhance model performance. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods on six open-source LLMs and five widely used benchmarks. |