LC-Eval: A Bilingual Multi-Task Evaluation Benchmark for Long-Context Understanding

ACL ARR 2025 February Submission4600 Authors

15 Feb 2025 (modified: 09 May 2025)ACL ARR 2025 February SubmissionEveryoneRevisionsBibTeXCC BY 4.0
Abstract:

Recent advancements in Large Language Models (LLMs) have demonstrated sophisticated capabilities, including the ability to process and comprehend extended contexts. These emergent capabilities necessitate rigorous evaluation methods to effectively assess their performance in long-context understanding. In this paper, we present LC-Eval, a bilingual, multi-task evaluation benchmark designed to evaluate long-context understanding in English and Arabic, targeting context lengths ranging from 4k to over 128k tokens. LC-Eval introduces four novel and challenging tasks: multi-document question answering, bilingual question answering, claim verification within a paragraph, and multiple-choice questions based on long contexts. These tasks are designed to assess LLMs' abilities in deep reasoning, document comprehension, information tracing, and bilingual information extraction and understanding. The benchmark includes datasets in both Arabic and English for each task, allowing for a comparative analysis of their performance across different text genres. Evaluations were conducted on both open-weight and closed LLMs, with results indicating that LC-Eval presents significant challenges. Even high-performing models, such as GPT-4o, struggled with certain tasks, highlighting the complexity and rigor of the benchmark.

Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: benchmarking, multilingual corpora, evaluation, datasets for low resource languages
Contribution Types: Model analysis & interpretability, Data resources
Languages Studied: Arabic, English
Submission Number: 4600
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