Zhengpeng Shi, Yanpeng Zhao, Jianqun Zhou, Yuxuan Wang, Qinrong Cui, Wei Bi, Song-Chun Zhu, Bo Zhao, Zilong Zheng. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Instruction-following capabilities in large language models (LLMs) have progressed significantly, enabling more complex user interactions through detailed prompts. However, retrieval systems have not matched these advances, most of them still relies on traditional lexical and semantic matching techniques that fail to fully capture user intent. Recent efforts have introduced instruction-aware retrieval models, but these primarily focus on intrinsic content relevance, which neglects the importance of customized preferences for broader document-level attributes. This study evaluates the instruction-following capabilities of various retrieval models beyond content relevance, including LLM-based dense retrieval and reranking models. We develop InfoSearch, a novel retrieval evaluation benchmark spanning six document-level attributes: Audience, Keyword, Format, Language, Length, and Source, and introduce novel metrics -- Strict Instruction Compliance Ratio (SICR) and Weighted Instruction Sensitivity Evaluation (WISE) to accurately assess the models' responsiveness to instructions. Our findings indicate that although fine-tuning models on instruction-aware retrieval datasets and increasing model size enhance performance, most models still fall short of instruction compliance. We release our dataset and code on https://github.com/EIT-NLP/InfoSearch.

Scene text editing aims to replace the source text with the target text while preserving the original background. Its practical applications span various domains, such as data generation and privacy protection, highlighting its increasing importance in recent years. In this study, we propose a novel Scene Text Editing network with Explicitly-decoupled text transfer and Minimized background reconstruction, called STEEM. Unlike existing methods that usually fuse text style, text content, and background, our approach focuses on decoupling text style and content from the background and utilizes the minimized background reconstruction to reduce the impact of text replacement on the background. Specifically, the text-background separation module predicts the text mask of the scene text image, separating the source text from the background. Subsequently, the style-guided text transfer decoding module transfers the geometric and stylistic attributes of the source text to the content text, resulting in the target text. Next, the background and target text are combined to determine the minimal reconstruction area. Finally, the context-focused background reconstruction module is applied to the reconstruction area, producing the editing result. Furthermore, to ensure stable joint optimization of the four modules, a task-adaptive training optimization strategy has been devised. Experimental evaluations conducted on two popular datasets demonstrate the effectiveness of our approach. STEEM outperforms state-of-the-art methods, as evidenced by a reduction in the FID index from 29.48 to 24.67 and an increase in text recognition accuracy from 76.8% to 78.8%.