Papers by Hung-Ting Su
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 . |
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. |
Unveiling Narrative Reasoning Limits of Large Language Models with Trope in Movie Synopses (2024.findings-emnlp)
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Hung-Ting Su, Ya-Ching Hsu, Xudong Lin, Xiang-Qian Shi, Yulei Niu, Han-Yuan Hsu, Hung-yi Lee, Winston Hsu
| Challenge: | Large language models (LLMs) equipped with chain-of-thoughts (CoT) prompting have shown significant multi-step reasoning capabilities in factual content like mathematics, commonsense, and logic. |
| Approach: | They introduce a trope-wise querying approach to assess the abstract reasoning abilities of large language models (LLMs) and uncover their low performance. |
| Outcome: | The proposed approach boosts the F1 score by 11.8 points and also reduces the performance of the large language models (LLMs) it also shows that it can cause hallucinations in narrative content, reducing the performance. |
VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions (2026.acl-long)
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| Challenge: | Existing Vision-and-Language Navigation benchmarks assume instructions are feasible and the referenced target exists. |
| Approach: | They propose a benchmark with false-premise instructions where the target is absent . they propose supervised room-level navigation with LLM/VLM-driven in-room exploration . |
| Outcome: | The proposed benchmark produces false-premise goals that are plausible but factually incorrect . ROAM achieves the best REV-SPL among compared methods, while baselines often under-explore and terminate prematurely under unreliable instructions. |