GPT-4 In-Context Learning Ability with Semantico-Syntactically Similar Examples in Russian

Abstract

In zero-shot performance with a dataset of over 2,200 Russian phrases and sentences, GPT-4 encounters difficulties to correctly identify the meaning of some examples. Therefore, the "problematic" examples are chosen for further investigation. To address these challenges, the in-context learning ability employed in GPTs can be utilized to enhance unsuccessful performance. This approach presumes providing semantico-syntactically similar examples beforehand. The experiment demonstrates that even with just one in-context example, GPT-4’s performance becomes more robust across nearly all problematic examples. However, the examples that remain misinterpreted potentially reveal that the model can underperform due to a lack of patterns in its training data.

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