Abstract
Reading comprehension tests are used in a variety of applications, reaching from education to assessing the comprehensibility of simplified texts. However, creating such tests manually and ensuring their quality is difficult and time-consuming. In this paper, we explore how large language models (LLMs) can be used to generate and evaluate multiple-choice reading comprehension items. To this end, we compiled a dataset of German reading comprehension items and developed a new protocol for human and automatic evaluation, including a metric we call text informativity, which is based on guessability and answerability. We then used this protocol and the dataset to evaluate the quality of items generated by Llama 2 and GPT-4. Our results suggest that both models are capable of generating items of acceptable quality in a zero-shot setting, but GPT-4 clearly outperforms Llama 2. We also show that LLMs can be used for automatic evaluation by eliciting item reponses from them. In this scenario, evaluation results with GPT-4 were the most similar to human annotators. Overall, zero-shot generation with LLMs is a promising approach for generating and evaluating reading comprehension test items, in particular for languages without large amounts of available data.- Anthology ID:
- 2024.readi-1.3
- Volume:
- Proceedings of the 3rd Workshop on Tools and Resources for People with REAding DIfficulties (READI) @ LREC-COLING 2024
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Rodrigo Wilkens, Rémi Cardon, Amalia Todirascu, Núria Gala
- Venues:
- READI | WS
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 22–37
- Language:
- URL:
- https://aclanthology.org/2024.readi-1.3
- DOI:
- Cite (ACL):
- Andreas Säuberli and Simon Clematide. 2024. Automatic Generation and Evaluation of Reading Comprehension Test Items with Large Language Models. In Proceedings of the 3rd Workshop on Tools and Resources for People with REAding DIfficulties (READI) @ LREC-COLING 2024, pages 22–37, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Automatic Generation and Evaluation of Reading Comprehension Test Items with Large Language Models (Säuberli & Clematide, READI-WS 2024)
- PDF:
- https://preview.aclanthology.org/landing_page/2024.readi-1.3.pdf