EduVidQA: Generating and Evaluating Long-form Answers to Student Questions based on Lecture Videos (2025.emnlp-main)
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| Challenge: | This paper explores using Multimodal Large Language Models (MLLMs) to respond to student questions from online lectures . MLLM is a novel question answering task of real world significance . |
| Approach: | They propose to use Multimodal Large Language Models to automatically respond to student questions from online lectures by using a dataset of 5252 question-answer pairs from 296 computer science videos. |
| Outcome: | The proposed model can fine tune and fine tune questions from 296 computer science videos and show that students' preferences are important to the task. |
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| Challenge: | Multimodal large language models (MLLMs) can grasp the intention of a question and decomposing it to a series of visual recognition sub-tasks to find out the answer with the help of an agent. |
| Approach: | They propose a framework for multimodal large language models to grasp the intention of a question and decompose it into a series of visual recognition sub-tasks to find out the answer. |
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VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos (2025.acl-long)
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| Challenge: | Multimodal large language models (MLLMs) are used for video quality assessment, image captioning and video analysis. |
| Approach: | They propose a benchmark to evaluate MLLMs on AIGC videos using coherence validation, error awareness, error type detection and reasoning evaluation tasks. |
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EDU-CIRCUIT-HW: Evaluating Multimodal Large Language Models on Real-World University-Level STEM Student Handwritten Solutions (2026.findings-acl)
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| Challenge: | Multimodal Large Language Models (MLLMs) are a promising tool for traditional education but lack authentic and domain-specific benchmarks to accurately interpret student handwritten solutions. |
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Visual Question Decomposition on Multimodal Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing methods for question decomposition focus on unimodal language models, but question decomposing capability of Multimodal Large Language Models (MLLMs) has yet to be explored. |
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FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question Answering (2025.findings-emnlp)
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| Challenge: | Existing methods focus on single-hop, single-modality, or short texts, limiting real-world applications . despite advances in visual question answering, this multihop setting remains underexplored due to a lack of quality datasets. |
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SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark (2024.acl-short)
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Zhenwen Liang, Kehan Guo, Gang Liu, Taicheng Guo, Yujun Zhou, Tianyu Yang, Jiajun Jiao, Renjie Pi, Jipeng Zhang, Xiangliang Zhang
| Challenge: | SceMQA focuses on core science subjects including Mathematics, Physics, Chemistry, and Biology. |
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Hurdles to Progress in Long-form Question Answering (2021.naacl-main)
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| Challenge: | Long-form question answering (LFQA) involves retrieving documents relevant to a given question and using them to generate a paragraph-length answer. |
| Approach: | They propose a long-form question answering system that relies on sparse attention and contrastive retriever learning to achieve state-of-the-art performance on the ELI5 LFQA dataset. |
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CSLM: A Framework for Question Answering Dataset Generation through Collaborative Small Language Models (2024.findings-emnlp)
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| Challenge: | Collecting high-quality question-answer (QA) pairs is vital for training large language models, but computational demands and associated costs often render such approaches prohibitive for the average researcher. |
| Approach: | They propose a small-scaled, open-source solution that generates QA pairs from documents or raw corpora using large-scale models like Llama-70B. |
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EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs (2025.emnlp-main)
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| Challenge: | Existing models produce outputs that are too advanced or vague for younger learners and there are no standardized benchmarks to evaluate their ability to adapt across cognitive and developmental stages. |
| Approach: | They propose to use a benchmark to assess LLMs' ability to adapt to different grade levels and to use it to evaluate their model's performance. |
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Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA (2024.acl-long)
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Yue Fan, Jing Gu, Kaiwen Zhou, Qianqi Yan, Shan Jiang, Ching-Chen Kuo, Yang Zhao, Xinze Guan, Xin Wang
| Challenge: | Multipanel images are a common form of visual representations, and humans can achieve approximately 99% accuracy on these questions. |
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