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YandexGPT fine-tuned for Russian text simplification to CEFR levels

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method for simplifying Russian texts to specific CEFR proficiency levels (A1, A2, B1) using reinforcement learning. They fine-tuned the YandexGPT model with techniques like Rejection Sampling and Direct Preference Optimization (DPO) to better control text complexity. Their best model achieved high accuracy in predicting target CEFR levels and demonstrated strong performance in human evaluations for simplicity, fluency, and adequacy, particularly for A2 and B1 levels. AI

IMPACT This research could lead to more accessible educational materials and improved language learning tools by enabling automated text adaptation to specific proficiency levels.

RANK_REASON Academic paper detailing a new method for text simplification using LLMs and reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

YandexGPT fine-tuned for Russian text simplification to CEFR levels

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Academic paper detailing a new method for text simplification using LLMs and reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Kristina Mustakova, Alexey Sorokin ·

    CEFR-oriented Russian Text Simplification Using Reinforcement Learning

    arXiv:2610.27237v1 Announce Type: new Abstract: We present a study on controlled automatic simplification of Russian texts using preference-based optimization algorithms with a focus on adapting texts to the A1, A2 and B1 proficiency levels. We fine-tune YandexGPT using Rejection…

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