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README.md
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Large Language Models (LLMs) are increasingly applied across various fields due to their advancing capabilities in numerous natural language processing tasks. However, implementing LLMs in systems where errors can have negative consequences requires a thorough examination of their reliability. Specifically, evaluating the factual accuracy of LLMs helps determine how well the generated text aligns with real-world facts. Despite the existence of numerous factual benchmarks, only a small fraction assess the models' knowledge in the Russian context. Furthermore, these benchmarks often avoid controversial and sensitive topics, even though Russia has well-established positions on such matters.
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#### Main tasks:
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- Testing the factual knowledge of LLMs in Russian domains.
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- Assessing the sensitivity (provocativeness) of the questions.
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Large Language Models (LLMs) are increasingly applied across various fields due to their advancing capabilities in numerous natural language processing tasks. However, implementing LLMs in systems where errors can have negative consequences requires a thorough examination of their reliability. Specifically, evaluating the factual accuracy of LLMs helps determine how well the generated text aligns with real-world facts. Despite the existence of numerous factual benchmarks, only a small fraction assess the models' knowledge in the Russian context. Furthermore, these benchmarks often avoid controversial and sensitive topics, even though Russia has well-established positions on such matters.
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### Contacts for cooperation
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If you have any questions, suggestions or are interested in cooperation, do not hesitate to contact us by email: [email protected]
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#### Main tasks:
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- Testing the factual knowledge of LLMs in Russian domains.
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- Assessing the sensitivity (provocativeness) of the questions.
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