A Multi-Agent Framework for Feature-Constrained Difficulty Control in Reading Comprehension Item Generation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866916025629409280 |
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| author | Hwang, Seonjeong Seo, Jun Kim, Hyounghun Lee, Gary Geunbae |
| author_facet | Hwang, Seonjeong Seo, Jun Kim, Hyounghun Lee, Gary Geunbae |
| contents | Recent studies in difficulty-controlled reading comprehension item generation have leveraged large language models (LLMs) to produce items by adjusting difficulty-related features. However, existing methods typically rely on a single-agent prompting approach, which often fails to consistently satisfy specified feature constraints, resulting in items that deviate from the target difficulty level. To address this limitation, we introduce MAFIG, a Multi-agent Framework for Feature-constrained Item Generation, where multiple LLM agents and feature-specific evaluators collaborate to generate and iteratively revise items based on intended constraints. Furthermore, to verify the efficacy of MAFIG in difficulty control, we propose a method for constructing a sequence of feature constraint sets that yield items with monotonically increasing difficulty. Experimental results demonstrate that MAFIG generates items that adhere to target constraints at a significantly higher rate than baselines, achieving robust difficulty control through the difficulty-calibrated constraint sequence. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_19316 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Multi-Agent Framework for Feature-Constrained Difficulty Control in Reading Comprehension Item Generation Hwang, Seonjeong Seo, Jun Kim, Hyounghun Lee, Gary Geunbae Computation and Language Recent studies in difficulty-controlled reading comprehension item generation have leveraged large language models (LLMs) to produce items by adjusting difficulty-related features. However, existing methods typically rely on a single-agent prompting approach, which often fails to consistently satisfy specified feature constraints, resulting in items that deviate from the target difficulty level. To address this limitation, we introduce MAFIG, a Multi-agent Framework for Feature-constrained Item Generation, where multiple LLM agents and feature-specific evaluators collaborate to generate and iteratively revise items based on intended constraints. Furthermore, to verify the efficacy of MAFIG in difficulty control, we propose a method for constructing a sequence of feature constraint sets that yield items with monotonically increasing difficulty. Experimental results demonstrate that MAFIG generates items that adhere to target constraints at a significantly higher rate than baselines, achieving robust difficulty control through the difficulty-calibrated constraint sequence. |
| title | A Multi-Agent Framework for Feature-Constrained Difficulty Control in Reading Comprehension Item Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.19316 |