LLM-based annotators using token-probability thresholds, RAG, and reasoning fine-tuning matched or exceeded human annotator quality on a proprietary 250-class intent-validation task.
The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design
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abstract
Embedding models play a crucial role in Natural Language Processing (NLP) by creating text embeddings used in various tasks such as information retrieval and assessing semantic text similarity. This paper focuses on research related to embedding models in the Russian language. It introduces a new Russian-focused embedding model called ru-en-RoSBERTa and the ruMTEB benchmark, the Russian version extending the Massive Text Embedding Benchmark (MTEB). Our benchmark includes seven categories of tasks, such as semantic textual similarity, text classification, reranking, and retrieval.The research also assesses a representative set of Russian and multilingual models on the proposed benchmark. The findings indicate that the new model achieves results that are on par with state-of-the-art models in Russian. We release the model ru-en-RoSBERTa, and the ruMTEB framework comes with open-source code, integration into the original framework and a public leaderboard.
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Large Language Models in the Task of Automatic Validation of Text Classifier Predictions
LLM-based annotators using token-probability thresholds, RAG, and reasoning fine-tuning matched or exceeded human annotator quality on a proprietary 250-class intent-validation task.