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The notable findings suggest that LLMs often make basic errors of grammar and semantics, and highlight the limitations of current LLMs as a reliable source of content generation and information retrieval for low-resource languages.
A two-layer LSTM language model trained on 250M morphemes of Hindi data can capture the relatively complex interaction between case and agreement in Hindi-Urdu, and it is shown that this model encodes case-marking linearly, implementing a geometrically intuitive and inter-pretable syntactic processing mechanism.
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