Papers by Xiaofeng Wu
Trident: Self-Supervised Preference Alignment via Triplet Regularization (2026.findings-acl)
Copied to clipboard
| Challenge: | Large vision-Language Models suffer from noisy supervision and semantic ambiguity in self-supervised settings. |
| Approach: | They propose a self-supervised framework that constructs reliable preference triplets . they propose 'trident' objective that enforces semantic separation between the triplet components . |
| Outcome: | The proposed framework outperforms state-of-the-art RLHF and RLAIF benchmarks on LLaVA-1.5-7B and achieves 95.70% precision on POPE using only 4k self-generated triplets and a single epoch. |
Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)
Copied to clipboard
Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠 林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang
| Challenge: | Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications. |
| Approach: | They propose a reliable strategy for domains to choose more robust LLMs for real-world applications. |
| Outcome: | The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications. |
CausalGaze: Unveiling Hallucinations via Counterfactual Graph Intervention in Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing classification-based methods capture noise and spurious correlations while overlooking the underlying causal mechanisms. |
| Approach: | They propose a hallucination detection framework based on structural causal models that captures static and passive signals from internal states and employs counterfactual interventions to disentangle causal reasoning paths from incidental noise. |
| Outcome: | Experiments on four datasets and three widely used LLMs show that the proposed framework improves AUROC and interpretability. |
TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data Synthesis (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) excel in natural language processing tasks but are vulnerable to harmful content and being exploited for malicious purposes. |
| Approach: | They propose a framework to measure the risk coverage of alignment datasets across three dimensions: Lexical Diversity, Malicious Intent, and Jailbreak Tactics. |
| Outcome: | The proposed framework measures risk coverage across Lexical Diversity, Malicious Intent, and Jailbreak Tactics. |
The Impact of Visual Information in Chinese Characters: Evaluating Large Models’ Ability to Recognize and Utilize Radicals (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing studies have examined whether large language models and vision-language models can harness these sub-character features in Chinese through prompting. |
| Approach: | They establish a benchmark to evaluate large language models' understanding of visual elements in Chinese characters, including radicals, composition structures, strokes, and stroke counts. |
| Outcome: | The proposed model exhibits some, but still limited, knowledge of the visual elements in Chinese characters regardless of whether images of characters are provided. |
Few Clean Instances Help Denoising Distant Supervision (2022.coling-1)
Copied to clipboard
| Challenge: | Existing distantly supervised entity relation extractors rely on noisy data for training and evaluation. |
| Approach: | They propose a criterion for clean instance selection based on influence functions to collect sample-level evidence for recognizing good instances. |
| Outcome: | The proposed method shows strong performance on real and synthetic noisy datasets. |
Shallow Focus, Deep Fixes: Enhancing Shallow Layers Vision Attention Sinks to Alleviate Hallucination in LVLMs (2025.emnlp-main)
Copied to clipboard
Xiaofeng Zhang, Yihao Quan, Chen Shen, Chaochen Gu, Xiaosong Yuan, Shaotian Yan, Jiawei Cao, Hao Cheng, Kaijie Wu, Jieping Ye
| Challenge: | Multimodal large language models (MLLMs) demonstrate excellent abilities for understanding visual information, but the hallucination remains a challenging problem. |
| Approach: | They propose a training-free approach to enhance vision attention sinks to facilitate convergence of the image token attention sink within shallow layers. |
| Outcome: | The proposed approach improves the convergence of the image token attention sink within shallow layers and strengthens the layer’s focus on the image itself. |