Papers by Wenbo Xu
Breaking Language Barriers: Cross-Lingual Continual Pre-Training at Scale (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have made significant strides towards Artificial General Intelligence, but training them from scratch is prohibitively expensive. |
| Approach: | They propose to continuously pre-train LLMs from existing pre-trained LLM models by using a set of parameters instead of randomly initializing them. |
| Outcome: | The proposed approach saves significant resources and accelerates convergence and performance. |
Recognizing Limits: Investigating Infeasibility in Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models fail to handle queries that exceed their knowledge and capabilities, leading to incorrect or fabricated responses. |
| Approach: | They conceptualize four main categories of infeasible tasks for LLMs which cover a broad spectrum of hallucination-related challenges identified in prior literature. |
| Outcome: | The proposed models can handle requests exceeding their knowledge and capabilities and refuse them . |
TS-SQL: Test-driven Self-refinement for Text-to-SQL (2025.findings-emnlp)
Copied to clipboard
| Challenge: | null |
| Approach: | null |
| Outcome: | null |
PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)
Copied to clipboard
Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei
| Challenge: | Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. |
| Approach: | They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios. |
| Outcome: | The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics. |
Double-Branch Multi-Attention based Graph Neural Network for Knowledge Graph Completion (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding methods cannot capture local and global information and are not designed well to learn representations of seen entities with sparse neighborhoods in isolated subgraphs. |
| Approach: | They propose a double-branch multi-attention based graph neural network to learn more expressive entity representations which contain rich global-local structural information. |
| Outcome: | The proposed method outperforms a general GNN-based approach for KGC. |
PATIMT-Bench: A Multi-Scenario Benchmark for Position-Aware Text Image Machine Translation in Large Vision-Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Current TIMT studies focus on providing translations for all text within an image, neglecting to provide bounding boxes and covering limited scenarios. |
| Approach: | They extend traditional TIMT into position-aware TIMt to support fine-grained translation . they introduce an Adaptive Image OCR Refinement Pipeline to refine results . |
| Outcome: | The proposed model supports fine-grained and layout-preserving translation . the experimental data highlight the scalability and generalizability of the model. |
Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing text-to-image models struggle to generate images with legible visual texts . current models lack support for Chinese texts, misspelling, and lack of diversity . |
| Approach: | They propose to empower backbone models to generate visual texts in Chinese and English . they propose to augment conventional training objective with glyph-aware training losses . |
| Outcome: | The proposed methods can generate visual texts in English and Chinese while maintaining image generation quality. |
The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training (2026.findings-acl)
Copied to clipboard
Rui Zhang, Hongwei Li, Yun Shen, Xinyue Shen, Wenbo Jiang, Guowen Xu, Yang Liu, Michael Backes, Yang Zhang
| Challenge: | Misaligned large language models can magnify harm by exploiting them to undermine safety . et al., 2022b; Bai e.t., 2023): misalignment, realignment and model-specific resistance are important . |
| Approach: | They evaluate four methods to identify a mechanism asymmetry between attack and defense . they find that ORPO is most effective for misalignment, but DPO excels in realignment . |
| Outcome: | The proposed methods show a mechanism asymmetry between attack and defense . the proposed methods excel in realignment, but at the expense of model utility . |
RAVR: Reference-Answer-guided Variational Reasoning for Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Experiments show that reinforcement learning (RL) can refine the reasoning abilities of large language models (LLMs) but requires a key prerequisite: the model must already be able to generate high-utility reasoning paths with non-negligible probability. |
| Approach: | They propose a framework that uses answer-conditioned reasoning as a variational surrogate for question-only reasoning. |
| Outcome: | Experiments on 11 benchmarks and 3 models show that RAVR reduces hesitation, strengthens conclusion consolidation, and promotes problem-specific strategies in reasoning. |
SATA: A Paradigm for LLM Jailbreak via Simple Assistive Task Linkage (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to design sophisticated instructions for the LLM to follow, or rely on multiple iterations, could hinder the performance and efficiency of jailbreaks. |
| Approach: | They propose a simple assistive task linkage paradigm which masks harmful keywords within malicious queries and uses a masked language model task to encode the semantics of the mangled keywords. |
| Outcome: | The proposed paradigm can effectively circumvent LLM safeguards and elicit harmful responses. |