Papers with COVER
Reasoning is All You Need for Video Generalization: A Counterfactual Benchmark with Sub-question Evaluation (2025.findings-acl)
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Qiji Zhou, YiFan Gong, Guangsheng Bao, Hongjie Qiu, Jinqiang Li, Xiangrong Zhu, Huajian Zhang, Yue Zhang
| Challenge: | Existing multimodal benchmarks often overlook counterfactual reasoning, which is crucial for robust video understanding. |
| Approach: | They propose a multidimensional multimodal benchmark that systematically evaluates MLLMs across the abstract-concrete and perception-cognition dimensions. |
| Outcome: | The proposed model decomposes complex queries into structured sub-questions, enabling fine-grained reasoning analysis. |
COVER: Context-Driven Over-Refusal Verification in LLMs (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have become increasingly prevalent in the field of Natural Language Processing (NLP), achieving unprecedented performance across linguistic tasks. |
| Approach: | They propose a framework to quantify and analyze context-driven over-refusal . they find that over-fusals depend on the task, system prompts, model family, and the number of retrieved documents. |
| Outcome: | The proposed framework quantifyes and analyzes the concept of context-driven over-refusal on two public corpora. |