Papers by Yuying Liu

5 papers
Rethinking Text-to-SQL: Dynamic Multi-turn SQL Interaction for Real-world Database Exploration (2026.findings-acl)

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Challenge: Structured Query Language (SQL) is the cornerstone for data-driven decision-making.
Approach: They propose a benchmark to rigorously evaluate Large Language Models within a dynamic interaction framework.
Outcome: The proposed benchmark aims to rigorously evaluate LLMs within a dynamic interaction framework.
RealChart2Code: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation (2026.acl-long)

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Challenge: Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains, but their ability to replicate complex, multi-panel visualizations remains largely unassessed.
Approach: They propose a large-scale benchmark to evaluate chart generation from large- scale raw data and assess iterative code refinement in a multi-turn conversational setting.
Outcome: The new benchmark evaluates 14 leading VLMs on real-world data and shows they struggle with complex plot structures and authentic data.
Beyond Single-View Detection: A Dual-Space Reasoning Framework for Interpretable Harmful Meme Understanding (2026.acl-long)

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Challenge: Existing methods for identifying harmful memes rely on modal alignment or black-box classifiers . BPDMoE-Hate provides visual explanations for viewpoint selection and hierarchical structuring .
Approach: They propose a framework that conceptualizes harmful meme detection as a process of "viewpoint decoupling and hierarchical fusion" they propose BPDMoE-Hate, which generates adversarial binary perspectives via VLMs and incorporates an adaptive viewpoint gating to facilitate viewpoint selection.
Outcome: The proposed framework surpasses existing methods in performance and provides visual explanations for viewpoint selection and hierarchical structuring.
HierDiffuse: Progressive Diffusion for Robust Interest Fusion in CTR Prediction (2025.emnlp-industry)

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Challenge: Existing approaches fuse long-term behavioral profiles and short-term interactions, suffering from representational misalignment and noise in transient signals.
Approach: They propose a framework that redefines interest fusion as a hierarchical denoising process through diffusion models.
Outcome: The proposed framework redefines interest fusion as a hierarchical denoising process through diffusion models.
GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal Reasoning (2026.findings-acl)

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Challenge: Recent reinforcement learning approaches have advanced reasoning in Large Language Models (LLMs), yet their adaptation to multimodal LLMs remains underexplored.
Approach: They propose a reinforcement learning framework that eliminates KL penalties and rewards consistency . they propose GRPO-CARE, which outperforms standard GR PO, with a base reward for accuracy and an adaptive bonus for consistency.
Outcome: The proposed framework outperforms standard GRPO on the most difficult evaluation level and reasoning consistency test benchmarks.

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