A Multi-Agent Framework for Feature-Constrained Difficulty Control in Reading Comprehension Item Generation (2026.acl-long)
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| Challenge: | Existing methods for difficulty-controlled reading comprehension item generation rely on a single agent prompting approach. |
| Approach: | They propose a multi-agent framework for Feature-constrained Item Generation where multiple LLM agents collaborate to generate and iteratively revise items based on intended constraints. |
| Outcome: | The proposed method generates items with monotonically increasing difficulty at higher rates than baselines. |
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