Long Input Benchmark for Russian Analysis
This addresses the need for proper long-context evaluation in Russian NLP, though it is incremental as it adapts existing datasets for a specific language.
The authors tackled the lack of evaluation for long-context understanding in Russian NLP by proposing LIBRA, a benchmark comprising 21 adapted datasets that assess models across context lengths from 4k to 128k tokens, with open-source resources and a leaderboard provided.
Recent advancements in Natural Language Processing (NLP) have fostered the development of Large Language Models (LLMs) that can solve an immense variety of tasks. One of the key aspects of their application is their ability to work with long text documents and to process long sequences of tokens. This has created a demand for proper evaluation of long-context understanding. To address this need for the Russian language, we propose LIBRA (Long Input Benchmark for Russian Analysis), which comprises 21 adapted datasets to study the LLM's abilities to understand long texts thoroughly. The tests are divided into four complexity groups and allow the evaluation of models across various context lengths ranging from 4k up to 128k tokens. We provide the open-source datasets, codebase, and public leaderboard for LIBRA to guide forthcoming research.