Challenge: Recent advances in Large Language Models (LLMs) have demonstrated sophisticated capabilities, including the ability to process and comprehend extended contexts.
Approach: They propose a bilingual, multi-task evaluation benchmark designed to evaluate long-context understanding in English and Arabic.
Outcome: The proposed benchmark targets context lengths ranging from 4k to over 128k tokens.

Similar Papers

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding (2024.acl-long)

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Challenge: Large language models (LLMs) can only handle texts a few thousand tokens long, limiting their applications on longer sequence inputs, such as books, reports, and codebases.
Approach: They propose a bilingual, multi-task benchmark for long context understanding that extends context windows and more sophisticated memory mechanisms to improve models' long context capabilities.
Outcome: The proposed model outperforms open-source models but struggles on longer contexts.
Marathon: A Race Through the Realm of Long Context with Large Language Models (2024.acl-long)

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Challenge: Existing long-context benchmarks do not accurately evaluate large language models’ comprehension and reasoning abilities in extended texts.
Approach: They propose a new evaluation benchmark that adopts a multiple-choice question format and uses a multi-choke question format to assess the comprehension and reasoning skills of large language models.
Outcome: The proposed benchmark provides a rapid, precise, and unbiased appraisal of the long-context comprehension skills of large language models.
BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models (2025.findings-emnlp)

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Challenge: Existing multilingual benchmarks focus primarily on language understanding tasks.
Approach: They develop a multi-way multilingual benchmark that measures critical capabilities of large language models across languages.
Outcome: Extensive experiments on BenchMAX reveal uneven utilization of core capabilities across languages, emphasizing the performance gaps that scaling model size alone does not resolve.
F-Eval: Asssessing Fundamental Abilities with Refined Evaluation Methods (2024.acl-long)

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Challenge: Large language models (LLMs) have been evaluated for their instruction-following capabilities but lack references to their fundamental abilities.
Approach: They propose a bilingual evaluation benchmark to evaluate the fundamental abilities of large language models including expression, commonsense and logic.
Outcome: The proposed evaluation methods show higher correlation coefficients and larger distinction than other evaluators.
Ada-LEval: Evaluating long-context LLMs with length-adaptable benchmarks (2024.naacl-long)

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Challenge: Existing long-text evaluation benchmarks, such as L-Eval and LongBench, focus on QA and summarization tasks.
Approach: They propose a length-adaptable benchmark for evaluating the long-context understanding of large language models.
Outcome: The proposed benchmarks do not cover ultralong settings (100k+ tokens) and are difficult to evaluate across different length ranges.
Ref-Long: Benchmarking the Long-context Referencing Capability of Long-context Language Models (2025.acl-long)

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Challenge: Long-context language models have impressive capabilities in long-contrast understanding tasks, but long-text referencing remains underexplored.
Approach: They propose a benchmark to assess long-context referencing capability of LCLMs . they use three subsets to test the model's ability to identify key indexes based on contextual relationships .
Outcome: The proposed benchmark assesses the long-context referencing capability of LCLMs.
MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)

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Challenge: Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities.
Approach: They propose a comprehensive benchmark covering 29 languages, built on an English benchmark.
Outcome: The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark.
Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? (2024.findings-eacl)

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Challenge: Large Language Models (LLMs) excel in various tasks, but their evaluation, especially in languages beyond the top 20, remains inadequate due to existing benchmarks and metrics limitations.
Approach: They propose to use Large Language Models as evaluators to rank or score other models’ outputs by calibrating them against 20K human judgments across three text-generation tasks, five metrics, and eight languages.
Outcome: The proposed evaluation methods can be used to improve multilingual evaluation by calibrating them against 20K human judgments across three text-generation tasks, five metrics, and eight languages.
LCFO: Long Context and Long Form Output Dataset and Benchmarking (2025.findings-acl)

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Challenge: Using long text outputs to evaluate progress in summarization and summary expansion tasks is challenging.
Approach: They propose a framework for assessing gradual summarization and summary expansion capabilities across diverse domains.
Outcome: The proposed framework provides alignments between specific QA pairs and corresponding summaries in 7 domains.
Systematic Evaluation of Long-Context LLMs on Financial Concepts (2024.emnlp-industry)

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Challenge: Long-context large language models (LC LLMs) are promising for tasks with long context windows . however, their ability to reliably utilize their growing context windows remains under investigation .
Approach: They evaluate the performance of long-context large language models using a real-world financial news dataset.
Outcome: The proposed models exhibit brittleness at longer context lengths even for simple tasks, the authors show . they advocate for more rigorous evaluation of LC LLMs by employing holistic metrics such as F1 (rather than recall)

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