Beyond Facts- Benchmarking Distributional Reading Comprehension in Large Language Models (2026.findings-acl)
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Pei-Fu Guo, Ya An Tsai, Chun-Chia Hsu, Kai-Xin Chen, Yun-Da Tsai, Kai-Wei Chang, Nanyun Peng, Mi-Yen Yeh, Shou-De Lin
| Challenge: | Existing reading comprehension benchmarks focus on factual information, but many real-world tasks require distributional knowledge expressed across text. |
| Approach: | They propose a reading comprehension benchmark for LLMs to evaluate their ability to infer distributional knowledge from natural language. |
| Outcome: | Experiments with multiple LLMs show that the model outperforms baselines, but performance varies widely across distribution types and characteristics. |
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