Papers by Zixin Ding
Automatic Prompt Engineering for Scalable Prompt Inversion in Text-to-Image Ad Generation (2026.acl-industry)
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| Challenge: | PRISM-DUEL is a black-box framework that formalizes prompt optimization as Automatic Prompt Engineering (APE) PRIMS-DUEl is motivated by advertising workflows requiring low-latency, diverse variants faithful to a human-designed ad. |
| Approach: | They propose a black-box framework that formalizes prompt optimization as Automatic Prompt Engineering (APE) they obtain label-free pairwise preferences and rationales from an LLM judge over pairs of generated images and use a dueling-bandit optimizer to optimize a prompt for generating controlled variations while matching the reference ad's visual content. |
| Outcome: | The proposed framework preserves visual similarity and semantic faithfulness while increasing diversity. |
A Novel Matching Paradigm: Unified Generative and Discriminative LLM with Prompt Compression for Relevance Learning (2026.acl-industry)
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Guoliang Zhao, Zixin Cui, Chao Ye, Dengwu He, Fei Huang, Yubo Liu, Shuanglong Li, Tzungren Kuo, Bin Ding, Shuang Zhang, null KunhongZhu, Zhi Guo, Liu Lin
| Challenge: | Existing approaches to matching use Large Language Models as feature extractors, underutilizing their full modeling capabilities. |
| Approach: | They propose a matching paradigm that integrates two-tower, single-towing, and generative tasks within a unified LLM framework via attention-mask partitioning. |
| Outcome: | The proposed model achieves superior performance and strong practical value in an industrial search engine. |
For-Value: Efficient Forward-Only Data Valuation for finetuning LLMs and VLMs (2026.acl-long)
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Wenlong Deng, Qi Zeng, Jiaming Zhang, Minghui Chen, Zixin Ding, Christos Thrampoulidis, Boying Gong, Xiaoxiao Li
| Challenge: | Existing methods for data valuation rely on gradient computations, making them prohibitive for billion-parameter models. |
| Approach: | They propose a forward-only data valuation framework that enables efficient batch-scalable value estimation while maintaining effectiveness. |
| Outcome: | The proposed framework matches or outperforms gradient-based baselines in detecting influential data and mislabeled data while achieving significant efficiency improvements. |
AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images (2026.acl-long)
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Bo Zhang, Tzu-Yen Ma, Zichen Tang, Junpeng Ding, Zirui Wang, Yizhuo Zhao, Peilin Gao, Zijie Xi, Zixin Ding, Haiyang Sun, Haocheng Gao, Yuan Liu, Liangjia Wang, Yiling Huang, Yujie Wang, Yuyue Zhang, Ronghui Xi, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Haihong E
| Challenge: | AEGIS examines whether current models can effectively audit AI-generated images in academic papers. |
| Approach: | They propose a holistic benchmark for forensic analysis of AI-Generated academic ImageS that reveals limitations in academic image forensics. |
| Outcome: | AEGIS compared with existing benchmarks on seven academic categories and features key advances in forensic analysis. |