| Challenge: | Using pre-trained language models, we find that the accuracy of LMs is far below human performance. |
| Approach: | They propose a benchmark of Sino LINGuistics which consists of 38K sentence pairs in Mandarin Chinese grouped into 9 high-level linguistic phenomena. |
| Outcome: | The proposed model performs better on local phenomena than hierarchical models and has a strong gender and number bias. |
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| Challenge: | Linguistically informed analyses of language models (LMs) contribute to understanding and improvement of such models. |
| Approach: | They introduce a corpus of Chinese linguistic minimal pairs (CLiMP) to investigate what knowledge Chinese LMs acquire. |
| Outcome: | The proposed corpus of Chinese linguistic minimal pairs (CLiMP) covers 9 major Chinese linguist phenomena. |
A Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese (2026.tacl-1)
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Yikang Liu, Yeting Shen, Hongao Zhu, Lilong Xu, Zhiheng Qian, Siyuan Song, Kejia Zhang, Jialong Tang, Pei Zhang, Baosong Yang, Rui Wang, Hai Hu
| Challenge: | Using sub-linear length normalized log-probabilities (SLLN-LP), we find unequal lengths of sentences in minimal pairs difficult for LMs even up to 32B parameters. |
| Approach: | They propose to use ZhoBLiMP as a linguistic minimal pair benchmark for Chinese language models to mitigate biases. |
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Language Models at the Syntax-Semantics Interface: A Case Study of the Long-Distance Binding of Chinese Reflexive Ziji (2025.coling-main)
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| Challenge: | Existing language models tend to rely heavily on sequential cues, but not always favoring the closest strings. |
| Approach: | They construct a dataset of 320 synthetic sentences and 360 natural sentences from the BCC corpus . they evaluate 21 language models against this dataset and compare their performance to native Mandarin speakers . |
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Revisiting a Pain in the Neck: A Semantic Reasoning Benchmark for Language Models (2026.acl-long)
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| Challenge: | Semantic phrases (SP) are lexical combinations whose meanings or usages may not be fully derived from their individual components. |
| Approach: | They propose to consolidate existing multiword expression resources into a unified testbed to assess language models in semantic phrase processing tasks. |
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LinguaLens: Towards Interpreting Linguistic Mechanisms of Large Language Models via Sparse Auto-Encoder (2025.emnlp-main)
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| Challenge: | Prior research on linguistic mechanisms of large language models is limited by coarse granularity, limited analysis scale, and narrow focus. |
| Approach: | They propose a framework for analyzing the linguistic mechanisms of large language models based on Sparse Auto-Encoders. |
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BLiMP: The Benchmark of Linguistic Minimal Pairs for English (2020.tacl-1)
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Alex Warstadt, Alicia Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, Samuel R. Bowman
| Challenge: | Recent studies have examined how linguistic knowledge of language models (LMs) varies across English phenomena. |
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Developing and Utilizing a Large-Scale Cantonese Dataset for Multi-Tasking in Large Language Models (2025.findings-emnlp)
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Jiyue Jiang, Alfred Kar Yin Truong, Yanyu Chen, Qinghang Bao, Sheng Wang, Pengan Chen, Jiuming Wang, Lingpeng Kong, Yu Li, Chuan Wu
| Challenge: | Cantonese is considered a low-resource language due to the dominance of Mandarin . rich colloquial vocabulary of Cantone, English loanwords, and code-switching characteristics add to the complexity of corpus collection and processing. |
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From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)
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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong
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UrBLiMP: A Benchmark for Evaluating the Linguistic Competence of Large Language Models in Urdu (2026.findings-acl)
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| Challenge: | Evaluating how large language models capture grammatical structure of low-resource languages remains underexplored. |
| Approach: | They evaluate a set of 5,696 minimal pairs that contrast grammatical acceptability across ten core syntactic and morpho-syntactical phenomena in Urdu. |
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How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language Models (2025.findings-naacl)
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| Challenge: | Cantonese has scant representation in NLP research, especially compared to other languages from similarly developed regions. |
| Approach: | They propose to evaluate Cantonese LLM performance in factual generation, mathematical logic, complex reasoning, and general knowledge in Cantonesian. |
| Outcome: | The proposed models will evaluate Cantonese's performance in factual generation, mathematical logic, complex reasoning, and general knowledge in Cantone. |