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license: mit
language:
- ru
tags:
- nlp
- benchmark
- russian
- long-context
- evaluation
LIBRA: Long Input Benchmark for Russian Analysis
Dataset Summary
LIBRA (Long Input Benchmark for Russian Analysis) is designed to evaluate the capabilities of large language models (LLMs) in understanding and processing long texts in Russian. This benchmark includes 21 datasets adapted for different tasks and complexities. The tasks are divided into four complexity groups and allow evaluation across various context lengths ranging from 4k up to 128k tokens.
Dataset Structure
The datasets are divided into subsets based on context lengths: 4k, 8k, 16k, 32k, 64k, and 128k tokens. Each subset contains a different number of samples depending on the task complexity.
Tasks and Complexity Groups
Group I: Simple Information Retrieval
- Passkey: Extract a relevant piece of code number from a long text fragment. Based on the original PassKey test from the m LongLLaMA’s GitHub repo.
- PasskeyWithLibrusec: Similar to Passkey but with added noise from Librusec texts.
Group II: Question Answering and Multiple Choice
- MatreshkaNames: Identify the person in dialogues based on the discussed topic. We used Matreshka dataset and Russian Names dataset to create this and the next task.
- MatreshkaYesNo: Indicate whether a specific topic was mentioned in the dialog.
- LibrusecHistory: Answer questions based on historical texts. Ideologically similiar to the PassageRetrieval dataset from LongBench.
- ruTREC: Few-shot in-context learning for topic classification. Created by translating the TREC dataset from LongBench.
- ruSciFi: Answer true/false based on context and general world knowledge. Translation of SciFi dataset from L-Eval which originally was based on SF-Gram.
- ruSciAbstractRetrieval: Retrieve relevant paragraphs from scientific abstracts.
- ruTPO: Multiple-choice questions similar to TOEFL exams. Translation of the TPO dataset from L-Eval.
- ruQuALITY: Multiple-choice QA tasks based on detailed texts. Created by translating the QuALITY dataset from L-Eval.
Group III: Multi-hop Question Answering
- ruBABILongQA: 5 long-context reasoning tasks for QA using facts hidden among irrelevant information.
- LongContextMultiQ: Multi-hop QA based on Wikidata and Wikipedia.
- LibrusecMHQA: Multi-hop QA requiring information distributed across several text parts.
- ru2WikiMultihopQA: Translation of the 2WikiMultihopQA dataset from LongBench.
Group IV: Complex Reasoning and Mathematical Problems
- ruSciPassageCount: Count unique paragraphs in a long text. Uses the basic idea of the original PassageCount dataset from LongBench.
- ruQasper: Question Answering over academic research papers. Created by translating the Qasper dataset from LongBench.
- ruGSM100: Solve math problems using Chain-of-Thought reasoning. Created by translating the GSM100 dataset from L-Eval.
Metrics
We use Exact Match (EM) and F1 metrics in our tests. EM metric is used to evaluate the accuracy of the model's responses by comparing the predicted answers to the ground truth. It is particularly effective for tasks where precise matching of responses is critical, such as question answering and retrieval tasks.
Citation
@misc{churin2024longinputbenchmarkrussian,
title={Long Input Benchmark for Russian Analysis},
author={Igor Churin and Murat Apishev and Maria Tikhonova and Denis Shevelev and Aydar Bulatov and Yuri Kuratov and Sergei Averkiev and Alena Fenogenova},
year={2024},
eprint={2408.02439},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2408.02439},
}
GitHub
For more details and code, please visit our GitHub repository.