Minimal pairs are a well-established approach to evaluating the grammatical
knowledge of language models. However, existing resources for minimal pairs
address a limited number of languages and lack diversity of language-specific
grammatical phenomena. This paper introduces the Russian Benchmark of
Linguistic Minimal Pairs (RuBLiMP), which includes 45k pairs of sentences that
differ in grammaticality and isolate a morphological, syntactic, or semantic
phenomenon. In contrast to existing benchmarks of linguistic minimal pairs,
RuBLiMP is created by applying linguistic perturbations to automatically
annotated sentences from open text corpora and carefully curating test data. We
describe the data collection protocol and present the results of evaluating 25
language models in various scenarios. We find that the widely used language
models for Russian are sensitive to morphological and agreement-oriented
contrasts but fall behind humans on phenomena requiring understanding of
structural relations, negation, transitivity, and tense. RuBLiMP, the codebase,
and other materials are publicly available.

这篇论文介绍了俄语语言最小对对比基准（RuBLiMP），它包含 45k 对的句子，这些句子在语法、形态、句法或语义现象上存在差异，与现有的语言最小对对比基准相比，RuBLiMP 利用了来自开放文本语料库的自动注释句子并仔细筛选测试数据。作者描述了数据收集协议，并展示了对 25 种语言模型在不同场景下的评估结果。研究发现，对于需要理解结构关系、否定、及时态的现象，俄语广泛使用的语言模型在形态和一致性方面较为敏感，但在这些现象上与人类相比存在一定差距。RuBLiMP、代码和其他材料可公开获取。

俄文语言最小对的评估基准：RuBLiMP

RuBLiMP: Russian Benchmark of Linguistic Minimal Pairs

Linguistically informed analyses of language models (LMs) contribute to the
understanding and improvement of these models. Here, we introduce the corpus of
Chinese linguistic minimal pairs (CLiMP), which can be used to investigate what
knowledge Chinese LMs acquire. CLiMP consists of sets of 1,000 minimal pairs
(MPs) for 16 syntactic contrasts in Mandarin, covering 9 major Mandarin
linguistic phenomena. The MPs are semi-automatically generated, and human
agreement with the labels in CLiMP is 95.8%. We evaluated 11 different LMs on
CLiMP, covering n-grams, LSTMs, and Chinese BERT. We find that classifier-noun
agreement and verb complement selection are the phenomena that models generally
perform best at. However, models struggle the most with the ba construction,
binding, and filler-gap dependencies. Overall, Chinese BERT achieves an 81.8%
average accuracy, while the performances of LSTMs and 5-grams are only
moderately above chance level.

介绍了汉语语言模型所获得的知识的研究方法。使用汉语语言的最小对称集构建数据集，评估 11 种不同的语言模型。结果显示，汉语 BERT 在平均精度达到 81.8％，而 LSTM 和 5-grams 的性能仅略高于随机水平。