Papers by Xiaoliang Chen
Penetrating Linguistic Disguises: A Slang-aware Label-Aligned Framework for Fine-Grained Toxicity Extraction in Chinese Hate Speech Detection (2026.findings-acl)
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| Challenge: | Flexible word boundaries and linguistic obfuscation, particularly slang, challenge precise span-level hate speech detection in Chinese. |
| Approach: | They propose a Slang-aware Label-Aligned Framework that maps slang to explicit hate semantics and uses task-specific branches to mitigate feature interference. |
| Outcome: | The proposed framework reduces ambiguity by mapping obscure slang to explicit hate semantics. |
Do LLMs Behave as Claimed? Investigating How LLMs Follow Their Own Claims using Counterfactual Questions (2025.emnlp-main)
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| Challenge: | Existing evaluation frameworks rely on curated datasets that, once public, may be accessed by newer LLMs. |
| Approach: | They propose a framework that generates counterfactual questions and answers from existing evaluation datasets and uses them to evaluate LLMs. |
| Outcome: | The proposed evaluation framework reduces the risk of data leakage by allowing the LLMs to respond to counterfactual questions and verify their claims. |
Mitigating Spurious Correlations in Text Classification Using Latent Space Geometry (2026.acl-long)
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| Challenge: | Existing models rely on predictive shortcuts that hold in training data but break under distribution shifts, leading to large performance drops for minority groups. |
| Approach: | They propose a framework that transforms abstract biases into interpretable geometric anchors without auxiliary classifiers by manipulating latent space geometry. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines and improves worst-group accuracy by over 20% on the CivilComments dataset. |
CodeRise: Bootstrapping LLMs for Ultra Low-Resource Programming Languages via Progressive Self-Refinement Curriculum (2026.findings-acl)
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| Challenge: | Existing methods for training data generation for low-resource languages suffer from a cold-start problem and lack diversity. |
| Approach: | They propose a two-stage framework that generates a high-quality, diverse, and progressively complex curriculum for Ultra Low-Resource Programming Languages (ULRPLs) they leverage the full formal syntax of the target language as structural guidance and apply a biased sampling strategy over library modules. |
| Outcome: | The proposed framework outperforms training-free and training-based baselines on two ULRPLs, Tengo and Janet. |
Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning (2025.findings-acl)
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Yuxia Geng, Runkai Zhu, Jiaoyan Chen, Jintai Chen, Xiang Chen, Zhuo Chen, Shuofei Qiao, Yuxiang Wang, Xiaoliang Xu, Sheng-Jun Huang
| Challenge: | Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL). |
| Approach: | They propose a solution that takes multiple compositions as inputs and constrains disentangled primitive features to be general across compositions. |
| Outcome: | The proposed architecture significantly improves performance on three popular CZSL benchmarks and has been verified by solid ablation studies. |
MPR-GUI: Benchmarking and Enhancing Multilingual Perception and Reasoning in GUI Agents (2026.acl-long)
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Ruihan Chen, Qiming Li, Xiaocheng Feng, Weihong Zhong, Xiaoliang Yang, Yuxuan Gu, Zekun Zhou, Yunfei Lu, Haoyu Ren, Kun Chen, Dandan Tu, Bing Qin
| Challenge: | Existing GUI benchmarks lack fine-grained diagnostics to identify which capabilities lead to task failures. |
| Approach: | They propose a multilingual P R GUI Benchmark to assess LVLMs' language capabilities . they propose XLI to align non-English hidden states with English ones during inference . |
| Outcome: | The proposed benchmark reveals consistent gaps between English and non-English settings . it reduces the cross-lingual gaps with an average gain of 6.5% in non- English settings compared to static benchmarks . |