LC-Eval: A Bilingual Multi-Task Evaluation Benchmark for Long-Context Understanding
This addresses the need for rigorous evaluation methods for long-context capabilities in LLMs, though it is incremental as it builds on existing benchmarking efforts.
The authors tackled the problem of evaluating long-context understanding in large language models by introducing LC-Eval, a bilingual multi-task benchmark for English and Arabic with context lengths up to 128k tokens, and found that even high-performing models like GPT-4o struggled on its challenging tasks.
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 \textbf{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.