Agenda-Driven Question Generation: A Case Study in the Courtroom Domain (2024.lrec-main)
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| Challenge: | Existing automated question generation methods focus on unstructured text and lack agenda and background documents as context. |
| Approach: | They propose to leverage large language models for CourtQG by fine-tuning them on two auxiliary tasks, agenda explanation and question type prediction. |
| Outcome: | The proposed method generates better questions according to standard metrics when compared to several baselines. |
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| Challenge: | Existing work suffers from mismatching between question type and answer . existing work fails to generate questions with type how while answer is personal name . |
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| Challenge: | Large Language Models (LLMs) have made remarkable strides in language generation, but they encounter difficulties in the knowledge-intensive legal domain. |
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| Challenge: | Existing QG datasets are not suitable for educational question generation because the questions are not real questions asked by humans during learning. |
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Learning to Generate Question by Asking Question: A Primal-Dual Approach with Uncommon Word Generation (2022.emnlp-main)
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| Challenge: | Existing automatic question generation methods focus on encoding passage and answer to generate question. |
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| Challenge: | Socratic questioning is a form of reflective inquiry often employed in education to encourage critical thinking in students. |
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Ask To The Point: Open-Domain Entity-Centric Question Generation (2023.findings-emnlp)
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| Challenge: | *entity-centric question generation (ECQG) is a task motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking. |
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| Challenge: | Existing QG systems perform substantially worse in answering multi-hop questions than single-hop ones. |
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SGCM: Salience-Guided Context Modeling for Question Generation (2024.lrec-main)
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| Challenge: | Identifying relevant sentences to answers is crucial for reasoning the possible questions before generation. |
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