Papers by Eric Chang
VISREAS: Complex Visual Reasoning with Unanswerable Questions (2024.findings-acl)
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| Challenge: | Logic2Vision is a visual question-answering dataset that validates question authenticity with the corresponding image and then reasoning over it. |
| Approach: | They propose a compositional visual question-answering dataset, VisReas, that consists of answerable and unanswerable visual queries . they use visual genome scene graphs to generate the query and the reasoning steps to generate it. |
| Outcome: | The proposed model outperforms generative models and the existing classification models and outperformed existing models. |
MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation (2023.emnlp-main)
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| Challenge: | Existing medical datasets require high quality domain-specific datasets. |
| Approach: | They propose a multi-level, multi-task, and multi-domain medical benchmark to facilitate the development of language models for healthcare. |
| Outcome: | The proposed model provides granular potential usage and supports a wide range of tasks. |
Selective Demonstrations for Cross-domain Text-to-SQL (2023.findings-emnlp)
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| Challenge: | Large language models with in-context learning have demonstrated impressive generalization capabilities in the cross-domain text-to-SQL task without the use of in-domain annotations. |
| Approach: | They propose a demonstration selection framework that utilizes both out-of-domain examples and synthetically generated in-domain demonstration examples to construct demonstrations. |
| Outcome: | The proposed framework outperforms baseline methods on two cross-domain text-to-SQL datasets with improvements of 1.1 and 11.8 points in execution accuracy. |
MARS: Unleashing the Power of Speculative Decoding via Margin-Aware Verification (2026.findings-acl)
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Jingwei Song, Xinyu Wang, Hanbin Wang, Xiaoxuan Lei, Tianyu Shi, Shixin Han, Eric Yang, Xiao-Wen Chang, Lynn Ai
| Challenge: | Autoregressive large language models suffer from high inference latency due to memorybandwidth constraints. |
| Approach: | They propose a method that decouples generation and verification by decoupling tokens and a lightweight draft model. |
| Outcome: | The proposed method delivers consistent and significant speedups over state-of-the-art baselines while preserving generation quality across diverse benchmarks. |
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation (2021.findings-emnlp)
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| Challenge: | Radiology report generation aims at generating descriptive text from radiology images automatically. |
| Approach: | They propose a weakly supervised contrastive loss method that generates descriptive text from radiology images automatically. |
| Outcome: | The proposed method outperforms previous work on correctness and text generation metrics for two public benchmarks. |
Event Detection from Social Media for Epidemic Prediction (2024.naacl-long)
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Tanmay Parekh, Anh Mac, Jiarui Yu, Yuxuan Dong, Syed Shahriar, Bonnie Liu, Eric Yang, Kuan-Hao Huang, Wei Wang, Nanyun Peng, Kai-Wei Chang
| Challenge: | Social media is an easy-to-access platform providing timely updates about societal trends and events. |
| Approach: | They propose a framework to extract epidemic-related events from social media posts to provide early warnings. |
| Outcome: | The proposed framework can detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue while existing models fail miserably. |