Papers by Hung-Ting Chen
CaLMQA: Exploring culturally specific long-form question answering across 23 languages (2025.acl-long)
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| Challenge: | Despite rising global usage of large language models, their ability to generate *long-form* answers to *culturally specific* questions remains unexplored in many languages. |
| Approach: | They perform the first study of textual multilingual long-form QA by creating a dataset of culturally specific questions across 23 different languages. |
| Outcome: | The results show that the best models make critical surface-level errors for many languages and their understanding of diverse cultures. |
MovieCORE: COgnitive REasoning in Movies (2025.emnlp-main)
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Gueter Josmy Faure, Min-Hung Chen, Jia-Fong Yeh, Ying Cheng, Hung-Ting Su, Yung-Hao Tang, Shang-Hong Lai, Winston H. Hsu
| Challenge: | MovieCORE is a video question answering dataset that focuses on surface-level comprehension. |
| Approach: | They propose a video question-answer dataset that uses large language models as thought agents to generate and refine high-quality question-anchor pairs. |
| Outcome: | The proposed model improves model reasoning capabilities post-training by 25% . the proposed model is based on a large language model and is scalable to a wide range of tasks . |
ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints (2026.acl-long)
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| Challenge: | Existing methods for embodied agents focus on directly executing instructions without considering whether objects can be manipulated. |
| Approach: | They propose a benchmark that evaluates embodied agents in dynamic environments . they use plug-and-play module that augments existing planners with explicit affordance reasoning . |
| Outcome: | The proposed benchmark evaluates embodied agents in dynamic environments with unpredictable affordances . ADAPT significantly improves robustness and task success across seen and unseen environments . |
Continually Improving Extractive QA via Human Feedback (2023.emnlp-main)
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| Challenge: | a study of extractive question answering systems using human feedback shows promising potential for continual learning. |
| Approach: | They study extractive question answering system by using user feedback to improve it . they design and deploy an iterative approach where users ask questions and provide feedback . |
| Outcome: | The proposed model improves over time across different data regimes and domains . human user feedback is more affordable and abundant than annotations provided by trained experts . |
OCID-Ref: A 3D Robotic Dataset With Embodied Language For Clutter Scene Grounding (2021.naacl-main)
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| Challenge: | Visual grounding (VG) is a crucial task in natural language processing, computer vision, and robotics. |
| Approach: | They propose a visual grounding task with referring expressions of occluded objects in a OCID-Ref dataset with 2,300 scenes and a point cloud input. |
| Outcome: | The proposed dataset shows that it can handle 2D and 3D signals but referring to occluded objects remains challenging for the modern visual grounding systems. |
Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence (2022.emnlp-main)
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| Challenge: | Existing work on question answering models relies on retrieved documents for provenance, but recent studies show that models can retain vast amounts of factual knowledge . retrieval-based generation approaches combine parametric knowledge sources with a large number of retrieved evidence documents, achieving state-of-the-art performance on open retrieval datasets. |
| Approach: | They propose to use parametric and parametric knowledge to generate free-form questions from retrieved evidence documents. |
| Outcome: | The proposed model can use parametric and parametric knowledge to generate free-form answers from retrieved evidence documents. |
Open-World Evaluation for Retrieving Diverse Perspectives (2025.naacl-long)
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| Challenge: | Existing retrieval systems only cover diverse perspectives on 33.74% of the examples . existing systems only focus on relevance to the question, ignoring diversity. |
| Approach: | They build a Benchmark for Retrieval Diversity for Subjective questions (BERDS) based on a question and diverse perspectives associated with the question . they evaluate retrievers paired with a corpus to determine whether each document contains a perspective . |
| Outcome: | The proposed approach improves retrieval diversity on complex questions . existing retrieval systems only cover diverse perspectives on 33.74% of the examples . |