Summary of Long Input Benchmark For Russian Analysis, by Igor Churin et al.
Long Input Benchmark for Russian Analysis
by Igor Churin, Murat Apishev, Maria Tikhonova, Denis Shevelev, Aydar Bulatov, Yuri Kuratov, Sergej Averkiev, Alena Fenogenova
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel benchmark, LIBRA (Long Input Benchmark for Russian Analysis), is proposed to evaluate Large Language Models’ (LLMs) ability to understand long texts in the Russian language. The benchmark consists of 21 datasets divided into four complexity groups, allowing evaluation across various context lengths ranging from 4k to 128k tokens. This development aims to address the need for proper evaluation of long-context understanding, particularly in NLP tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LIBRA is a new benchmark that helps us understand how well computer models can process and make sense of long texts written in Russian. It includes many datasets that are adapted to test different aspects of language understanding, like grammar and vocabulary. The goal is to see how well these computer models perform when they’re given longer and more complex texts to analyze. |
Keywords
» Artificial intelligence » Language understanding » Nlp
