Papers by Ying Xiao
The “Knowledge–Behavior Gap” in Cultural Taboo Safety of Large Language Models (2026.acl-long)
Copied to clipboard
Ying He, Sihang Jiang, Xingzhou Chen, Zhouhong Gu, Yiwei Gu, Minggui HE, Shimin Tao, null Mahongxia, Yanghua Xiao
| Challenge: | Existing cultural benchmarks assess cultural knowledge or values biases, but ignore cultural taboos. |
| Approach: | They propose a benchmark to evaluate and improve the cultural taboo safety of large language models. |
| Outcome: | The proposed benchmark spans 77 countries and regions, and includes over 2,020 taboos. |
Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents (2026.findings-acl)
Copied to clipboard
Ying He, Zhouhong Gu, Zhecheng Hu, Yubo Zhou, Hao Shen, Jiaqing Liang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao, Zhixu Li
| Challenge: | Existing LLMs struggle to identify errors in financial documents, a study shows . 18% of financial practitioners make errors daily, one-third make errors several times weekly, and 59% make errors multiple times monthly. |
| Approach: | They introduce FinED-Bench, a publicly available Benchmark for financial error detection . it covers nine real-world financial scenarios and includes over 900 documents in 2025 . supervised fine-tuning can significantly improve the performance of weaker LLMs, they show . |
| Outcome: | The proposed benchmark covers nine real-world financial scenarios and includes over 900 documents reported in 2025 that are unseen by existing language models. |
A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition (2023.findings-acl)
Copied to clipboard
Limao Xiong, Jie Zhou, Qunxi Zhu, Xiao Wang, Yuanbin Wu, Qi Zhang, Tao Gui, Xuanjing Huang, Jin Ma, Ying Shan
| Challenge: | Existing models for named entity recognition (NER) are based on large-scale labeled datasets, which always obtain using crowdsourcing. |
| Approach: | They propose a CONfidence-based partial Label Learning method to integrate prior and posterior confidences for crowd-annotated named entity recognition models. |
| Outcome: | The proposed model improves on real-world and synthetic datasets compared with baselines. |
EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models are reshaping modern software development, but they often incur substantial monetary cost. |
| Approach: | They propose an experience-driven early termination approach that extracts structured experience from prior issue-resolution executions and leverages it to guide early termination during patch generation and selection. |
| Outcome: | The proposed approach reduces cost by 19%–55% with negligible loss in resolution rate (at most 0.2%) EET extracts structured experience from prior issue-resolution executions and leverages it to guide early termination during patch generation and selection. |
Reasoning-Augmented Conversation for Multi-Turn Jailbreak Attacks on Large Language Models (2025.findings-emnlp)
Copied to clipboard
Zonghao Ying, Deyue Zhang, Zonglei Jing, Yisong Xiao, Quanchen Zou, Aishan Liu, Siyuan Liang, Xiangzheng Zhang, Xianglong Liu, Dacheng Tao
| Challenge: | Existing methods for implementing multi-turn jailbreaks struggle to balance semantic coherence with attack effectiveness, resulting in benign semantic drift or ineffective detection evasion. |
| Approach: | They propose a framework that reformulates harmful queries into benign reasoning tasks and leverages LLMs’ strong reasoning capabilities to compromise safety alignment. |
| Outcome: | The proposed framework achieves state-of-the-art attack effectiveness in complex conversational scenarios, with average ASRs increasing by up to 96%. |
LiGen: Active Lipid Generation via a Molecular Language Model (2026.acl-long)
Copied to clipboard
| Challenge: | Lipid nanoparticles (LNPs) can deliver cargos to tumor and immune cells . traditional approaches rely on experimental screening and expert judgment . |
| Approach: | They propose a method to generate lipid molecules efficiently and actively using deep learning. |
| Outcome: | The proposed method outperforms baseline methods on multiple cell lines and achieves a 30% improvement over the current methods. |
Data-Efficient Selection via Grammatical Complexity in Continual Pre-training of Domain-Specific LLMs (2025.emnlp-main)
Copied to clipboard
Yizhou Ying, Geng Zhang, Cui Danxin, Chengyu Du, Guanglei Yue, Sihang Jiang, Jiaqing Liang, Yifei Fu, Hailin Hu, Yanghua Xiao
| Challenge: | Existing data selection strategies for continual pre-training of large language models often rely on scarce labeled data or computationally expensive LLMs. |
| Approach: | They propose an annotation-independent data selection framework for CPT that evaluates grammatical complexity using lexical diversity and syntactic complexity. |
| Outcome: | The proposed framework outperforms baselines on a financial dataset and surpasses full-data training by 1.7% using only 20% of the data. |
From Remembering to Metacognition: Do Existing Benchmarks Accurately Evaluate LLMs? (2025.findings-emnlp)
Copied to clipboard
Geng Zhang, Yizhou Ying, Sihang Jiang, Jiaqing Liang, Guanglei Yue, Yifei Fu, Hailin Hu, Yanghua Xiao
| Challenge: | Existing benchmark datasets focus on low-level cognitive tasks while providing limited coverage of higher-level reasoning skills. |
| Approach: | They analyze the cognitive depth of popular LLM benchmarks using Bloom’s Taxonomy to evaluate both the cognitive and knowledge dimensions. |
| Outcome: | The results show that incorporating higher-level cognitive instructions into the current instruction fine-tuning process improves model performance. |
Cross-modal Contrastive Attention Model for Medical Report Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for medical report generation are unable to capture useful information from historical cases. |
| Approach: | They propose a model that captures both visual and semantic information from similar cases. |
| Outcome: | The proposed model outperforms the state-of-the-art models on almost all metrics on IU X-Ray and MIMIC-CXR benchmarks. |
Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to lexically constrained neural machine translation suffer from high latency. |
| Approach: | They propose a plug-in algorithm for non-autoregressive translation for this problem . they propose ACT to familiarize the model with the source-side context of constraints . |
| Outcome: | The proposed model improves over the backbone constrained NAT model in constraint preservation and translation quality, especially for rare constraints. |
APEX: Learning Adaptive Priorities for Multi-Objective Alignment in Vision-Language Generation (2026.findings-acl)
Copied to clipboard
Dongliang Chen, Xinlin Zhuang, Junjie Xu, Luojian Xie, Zehui Wang, Jiaxi Zhuang, Haolin Yang, Liang Dou, Xiao He, Xingjiao Wu, Ying Qian
| Challenge: | APEX optimizes for text-to-image generation by combining learning potential, conflict penalty, and progress need. |
| Approach: | They propose an algorithm that stabilizes heterogeneous rewards and dynamically schedules objectives . they propose a method that achieves better Pareto trade-offs across four heterogenous objectives based on P3 Adaptive Priorities . |
| Outcome: | The proposed algorithm achieves better pareto trade-offs across four heterogeneous objectives while maintaining competitive OCR accuracy. |
DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing adversarial training methods require multi-step gradient ascents or word substitutions to obtain adversarials, which impairs the effectiveness of adversariarial training. |
| Approach: | They propose a procedure for instead adversarial training with only clean data that estimates the adversarials loss by perturbing the input data’s probability distribution rather than their embeddings. |
| Outcome: | The proposed procedure reduces time consumption by up to 70% compared to current best-performing adversarial training methods. |