Papers by Yi-Li Hsu
Beyond Evidence: Belief-Chain Conditioning for Persuasive Misinformation Debunking Explanation (2026.findings-acl)
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| Challenge: | Existing methods to misinformation correction focus on relying on audience beliefs to generate factually accurate responses and to engage with users' mental states. |
| Approach: | They construct large language models with cognitive chains and use them to model their outputs on beliefs that engage with users' mental states. |
| Outcome: | The proposed model improves explanation quality for audiences with misinformation-aligned beliefs by incorporating believers’ chains into the model. |
Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification (2023.acl-long)
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| Challenge: | Existing models only address text classification problem in the euclidean space, which is not optimal . e.g., fear and terrified labels may not be differentiated in such space, harming performance . |
| Approach: | They propose a framework that can integrate hyperbolic embeddings to improve the task . they learn label embeddements in the hyperbolical space and then add them to the framework . |
| Outcome: | The proposed framework improves fine-grained emotion classification on two benchmark datasets with 3% improvement over previous state-of-the-art models. |
Illusions of the Gold Standard: A Large-scale Analysis of Human Evaluation Protocols for Long-form Text Generation (2026.acl-long)
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Katelyn X. Mei, Yi-Li Hsu, Minjoon Choi, Zongwan Cao, Chenjun Xu, Bingbing Wen, Su Lin Blodgett, Lucy Lu Wang
| Challenge: | a large-scale analysis of human evaluation protocols for long-form generation tasks is lacking in current practice . current protocols lack proper standardization and operationalization, which can limit validity of evaluation . |
| Approach: | They conduct a large-scale analysis of human evaluation protocols for long-form generation tasks in *CL conference papers from 2023–2025. |
| Outcome: | The proposed evaluation protocols lack standardization and operationalization, the authors show . they also find that the evaluation protocols are inadequate for specific domains and tasks . |
Is Explanation the Cure? Misinformation Mitigation in the Short Term and Long Term (2023.findings-emnlp)
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| Challenge: | Using natural language processing (NLP), there is an ongoing shift towards NLPbased solutions such as fake news detection and generation of fact-checked, counterfactual explanations. |
| Approach: | They compare the effectiveness of a warning label and state-of-the-art counterfactual explanations generated by natural language generation (GPT4) models in debunking misinformation. |
| Outcome: | The proposed explanations significantly decrease participants’ self-reported belief in fake claims for the short-term and long-term. |
Do Large Multimodal Models Solve Caption Generation for Scientific Figures? Lessons Learned from SciCap Challenge 2023 (2026.tacl-1)
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Ting-Yao Hsu, Yi-Li Hsu, Shaurya Rohatgi, Chieh-Yang Huang, Ho Yin Sam Ng, Ryan Rossi, Sungchul Kim, Tong Yu, Lun-Wei Ku, Clyde Lee Giles, Ting-Hao Huang
| Challenge: | SciCap dataset launched in 2021 aims to generate high-quality captions for scientific figures. |
| Approach: | They propose to use the SciCap dataset to develop models for captioning diverse figure types across various academic fields. |
| Outcome: | The proposed models showed impressive performance on the SciCap dataset and in various vision-and-language tasks. |
Enhancing Perception: Refining Explanations of News Claims with LLM Conversations (2024.findings-naacl)
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| Challenge: | a new framework for Large Language Models (LLMs) streamlines the task of crafting explanations for fake news . a study compared refinement conversations between human and LLMs to enhance the effectiveness of LLM explanations . |
| Approach: | They propose a framework for Large Language Models to streamline the task of crafting fake news explanations. |
| Outcome: | The proposed framework enhances the process of crafting explanations for fake news claims through conversational refinement. |