KoRC: Knowledge oriented Reading Comprehension Benchmark for Deep Text Understanding

TL;DR

KoRC is a new benchmark for deep text understanding that emphasizes broad knowledge coverage and flexible answers, revealing that current models still struggle with this challenging task.

Contribution

The paper introduces KoRC, a knowledge-oriented reading comprehension benchmark with extensive knowledge coverage and answer formats beyond spans or choices.

Findings

State-of-the-art models achieve only 68.3% in-distribution accuracy.

Models achieve only 30.0% F1 on out-of-distribution data.

Deep text understanding remains a significant challenge.

Abstract

Deep text understanding, which requires the connections between a given document and prior knowledge beyond its text, has been highlighted by many benchmarks in recent years. However, these benchmarks have encountered two major limitations. On the one hand, most of them require human annotation of knowledge, which leads to limited knowledge coverage. On the other hand, they usually use choices or spans in the texts as the answers, which results in narrow answer space. To overcome these limitations, we build a new challenging benchmark named KoRc in this paper. Compared with previous benchmarks, KoRC has two advantages, i.e., broad knowledge coverage and flexible answer format. Specifically, we utilize massive knowledge bases to guide annotators or large language models (LLMs) to construct knowledgable questions. Moreover, we use labels in knowledge bases rather than spans or choices as the final answers. We test state-of-the-art models on KoRC and the experimental results show that the strongest baseline only achieves 68.3% and 30.0% F1 measure in the in-distribution and out-of-distribution test set, respectively. These results indicate that deep text understanding is still an unsolved challenge. The benchmark dataset, leaderboard, and baseline methods are released in https://github.com/THU-KEG/KoRC.