Papers by Xiyang Huang
AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models (2024.findings-emnlp)
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Xiyang Wu, Tianrui Guan, Dianqi Li, Shuaiyi Huang, Xiaoyu Liu, Xijun Wang, Ruiqi Xian, Abhinav Shrivastava, Furong Huang, Jordan Boyd-Graber, Tianyi Zhou, Dinesh Manocha
| Challenge: | Large vision-language models are prone to hallucinations, where contextual cues in an image can trigger the language module to produce overconfident and incorrect reasoning about abnormal or hypothetical objects. |
| Approach: | They propose to automate the generation of hallucination-related questions using images . they propose to use three image manipulation strategies to induce hallucinosity . |
| Outcome: | The proposed approach reduces human bias in crafting such examples and improves accuracy. |
Language Agnostic Multilingual Information Retrieval with Contrastive Learning (2023.findings-acl)
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| Challenge: | Annotated training data is costly to obtain in many languages . |
| Approach: | They propose a semantic contrastive loss to align parallel sentences that share the same semantics in different languages and a language contrastive gain to leverage parallel sentence pairs to remove language-specific information from non-parallel corpora. |
| Outcome: | The proposed model improves retrieval performance while requiring less computational effort. |
CaDRL: Document-level Relation Extraction via Context-aware Differentiable Rule Learning (2025.coling-main)
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Kunli Zhang, Pengcheng Wu, Bohan Yu, Kejun Wu, Aoze Zheng, Xiyang Huang, Chenkang Zhu, Min Peng, Hongying Zan, Yu Song
| Challenge: | Existing methods for document-level relation extraction (DocRE) lack logic and transparency. |
| Approach: | They propose a Context-aware differentiable rule learning framework that learns the doc-specific logical rule to avoid suboptimal constraints. |
| Outcome: | The proposed framework outperforms existing rule-based frameworks on three DocRE datasets. |
CmEAA: Cross-modal Enhancement and Alignment Adapter for Radiology Report Generation (2025.coling-main)
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| Challenge: | Existing methods for automatic radiology report generation suffer from data bias. |
| Approach: | They propose a method that connects a vision encoder with a frozen large language model by using a cross-modal enhancement and alignment adapter. |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on IU X-Ray and MIMIC-CXR datasets. |