LIBRA: Long Input Benchmark for Russian Analysis

LIBRA

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 18 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 512k tokens.

For model comparison and results, see the LIBRA Leaderboard. The benchmark is described in detail in our paper.

NOTE: This is a new benchmark version released in May 2026. The original version (described in the paper) can be found here. We strongly encourage using the current version as it contains cleaned and extended datasets with ensured data quality.

LIBRA Mini

Running a full LIBRA evaluation can be prohibitively expensive and time-consuming due to the large number of datasets and long context lengths involved. Moreover, some of the included tasks have become less informative as benchmarks, with modern models achieving near-saturated scores on them.

To address this, we introduce LIBRA Mini — a compact, curated subset of 6 datasets selected from the full benchmark. These datasets represent the most challenging and diagnostically informative tasks in LIBRA, covering diverse task types and complexity levels (see Task Description for more information):

• ruBABILongQA3 — multi-fact reasoning over long contexts
• ruSciPassageCount — counting unique paragraphs in extended scientific texts
• LibrusecMHQA — multi-hop QA with information spread across multiple text parts
• LongContextMultiQ — multi-hop QA based on Wikidata and Wikipedia
• ru2WikiMultihopQA — multi-hop reasoning across multiple Wikipedia articles
• MatreshkaNames — identifying persons in dialogues based on discussed topics

LIBRA Mini uses the same Exact Match (EM) metric and evaluation methodology as the full benchmark. Results for LIBRA Mini are reported in a dedicated section on the leaderboard.

We recommend using LIBRA Mini as the primary evaluation suite for model comparisons, while the full LIBRA benchmark remains available for comprehensive analysis.

Dataset Structure

The datasets are divided into subsets based on context lengths. The table below shows the number of examples per context length for each dataset. Datasets included in LIBRA Mini are highlighted in bold. Note that not all datasets cover the full range of context lengths — some are designed for specific length ranges that best suit their task type.

Task	4k	8k	16k	32k	64k	128k	256k	512k		Total
— Group I —
passkey	200	200	200	200	200	200	200	200		1600
passkey_with_librusec	200	200	200	200	200	200	200	200		1600
— Group II —
librusec_history	-	32	32	32	32	-	-	-		128
matreshka_names	150	150	145	150	45	-	-	-		640
matreshka_yes_no	300	300	300	300	290	280	-	-		1770
ru_quality	-	18	184	-	-	-	-	-		202
ru_sci_abstract_retrieval	209	210	210	206	185	200	200	-		1420
ru_sci_fi	-	-	-	216	213	-	-	-		429
ru_tpo	-	900	-	-	-	-	-	-		900
— Group III —
librusec_mhqa	-	384	-	-	-	-	-	-		384
long_context_multiq	158	121	83	109	41	-	-	-		512
ru_2wikimultihopqa	-	147	384	369	-	-	-	-		900
ru_babilong_qa1	99	99	99	94	91	99	200	98		879
ru_babilong_qa2	85	76	77	73	65	99	200	98		773
ru_babilong_qa3	60	68	69	65	65	100	198	98		723
ru_babilong_qa4	78	85	83	87	75	99	200	98		805
ru_babilong_qa5	99	99	98	96	96	99	200	98		885
— Group IV —
ru_sci_passage_count	104	99	100	87	91	90	43	60		674

Task Description

The benchmark tasks are organized into four complexity groups, ranging from simple retrieval to complex reasoning. The grouping reflects both the cognitive difficulty of the task and the degree to which models must integrate information across the full context window. Group I serves as a basic sanity check, verifying that a model can process long inputs at all. Groups II and III progressively require deeper language understanding, multi-step reasoning, and the ability to locate and combine information from distant parts of the context. Group IV represents the most demanding tasks, requiring complex reasoning that goes beyond standard question answering formats. The total score on the leaderboard is computed across all four groups.

Group I: Simple Information Retrieval (sanity check)

This group includes the most simple tasks which serve as a sanity check for models to work with such amount of tokens.

• Passkey: Extract a relevant piece of code number from a long text fragment. Based on the original PassKey test from the LongLLaMA's GitHub repo.
• PasskeyWithLibrusec: Similar to Passkey but with added noise from Librusec texts.

Group II: Question Answering and Multiple Choice

This group consists of standard QA and multiple choice tasks adapted for the long-context setting.

• 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 similar to the PassageRetrieval 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

This group includes long-context multi-hop QA problems where the answer requires combining multiple pieces of information distributed across the context.

• ruBABILongQA (1-5): 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

This group includes the most complex long-context tasks, which span beyond multiple-choice and multi-hop QA. At this point Group IV comprises only one task and we invite the community to contribute to it.

• ruSciPassageCount: Count unique paragraphs in a long text. Uses the basic idea of the original PassageCount dataset from LongBench.

Metrics

We use Exact Match (EM) as a primary metric for all tasks. EM is used to evaluate the accuracy of the model's responses by comparing the predicted answers to the ground truth.

Changes from the Original Version

This version of LIBRA includes both automatic and manual improvements over the original release. All datasets underwent automatic quality filtering to ensure consistency and reliability of annotations. In addition, several datasets were manually revised and extended with the help of human annotators: LibrusecMHQA, LongContextMultiQ, MatreshkaNames, MatreshkaYesNo, ru2WikiMultihopQA, ruSciFi, and ruTPO received targeted corrections and additional examples. The datasets ruGSM100 and ruQasper were removed from the benchmark as they did not meet the updated quality criteria.

The maximum supported context length has been extended from 128k to 512k tokens. The following datasets now include examples at longer context lengths not present in the original version: ruBABILongQA (1–5), ruSciAbstractRetrieval, ruSciPassageCount, LongContextMultiQ, MatreshkaYesNo, Passkey, and PasskeyWithLibrusec.

Evaluation

Starting from this version, LIBRA supports evaluation via lm-evaluation-harness — a widely adopted framework for standardized LLM evaluation. Both full LIBRA and compact LIBRA Mini evaluations are supported.

To get started:

pip install lm-eval[vllm]

Run evaluation on the full LIBRA benchmark:

lm_eval --model vllm \
        --model_args pretrained=Qwen/Qwen3-30B-A3B,max_model_len=262144 \
        --tasks libra \
        --apply_chat_template \
        --device cuda:0

Run evaluation on LIBRA Mini only:

lm_eval --model vllm \
        --model_args pretrained=Qwen/Qwen3-30B-A3B,max_model_len=262144 \
        --tasks libra_mini \
        --apply_chat_template \
        --device cuda:0

For the full list of configuration options and instructions on adding new models, please refer to the lm-evaluation-harness documentation.

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}, 
}