We explore the use of large language models (LLMs) for morphological disambiguation in Belarusian, a low-resource language. The pipeline has two stages: a rule-based analyzer generates candidate lemmas and grammatical tags, which an LLM then disambiguates in context. Initial evaluation of ChatGPT, Claude, and Gemini on a gold-standard sample shows high accuracy. We scale this approach to a 375K-word corpus using Gemini and compare the results against a neural baseline (Stanza). Manual review of discrepancies suggests that the LLM-based approach outperforms the baseline, offering a solution for corpus annotation in Belarusian.

We examine how the capabilities of large language models (LLMs) have evolved on eight Belarusian language tasks contributed in 2023 to OpenAI’s Evals framework. We evaluate state-of-the-art models both on the original development sets and newly created test sets. Results demonstrate significant but non-uniform progress over this period: some tasks are almost saturated, while others show minor improvement beyond trivial baselines. Error analysis shows that certain challenges haven’t yet been addressed, e.g. misidentification of non-words as legitimate vocabulary, or conversion from modern to classical orthography. We release the datasets and the generated completions (https://doi.org/10.5281/zenodo.18163825).

In the epoch of multilingual large language models (LLMs), it is still challenging to evaluate the models’ understanding of lower-resourced languages, which motivates further development of expert-crafted natural language understanding benchmarks. We introduce BelarusianGLUE — a natural language understanding benchmark for Belarusian, an East Slavic language, with ≈15K instances in five tasks: sentiment analysis, linguistic acceptability, word in context, Winograd schema challenge, textual entailment. A systematic evaluation of BERT models and LLMs against this novel benchmark reveals that both types of models approach human-level performance on easier tasks, such as sentiment analysis, but there is a significant gap in performance between machine and human on a harder task — Winograd schema challenge. We find the optimal choice of model type to be task-specific: e.g. BERT models underperform on textual entailment task but are competitive for linguistic acceptability. We release the datasets (https://hf.co/datasets/maaxap/BelarusianGLUE) and evaluation code (https://github.com/maaxap/BelarusianGLUE).