Title: MGAL: A Multilingual Granularity-Aware Long-context Benchmark
Primary area: datasets and benchmarks
Keywords: Long-context Benchmark

Abstract: Evaluation of long-context Large Language Models (LLMs) has advanced rapidly. However, most existing benchmarks are limited to the document level and focus mainly on high-resource languages, leaving many fine-grained challenges insufficiently evaluated. To address this gap, we present MGAL, the first multilingual, granularity- and position-aware long-context benchmark. MGAL is constructed from United Nations (UN) reports spanning 8K to 128K tokens across the six official UN languages. It covers four coherent levels of linguistic granularity (word, sentence, paragraph, and document) and further stratifies entries by their position within the document (begin, middle, and end), indexed at both the document and paragraph levels. This design enables systematic diagnosis of multilingual long-context comprehension across different granularities. 

Through extensive experiments and analyses on 12 long-context LLMs, we find that: (1) LLMs perform well at word-level tasks but struggle with coarser-grained ones; and (2) Closed-source models retain a clear performance advantage in low-resource languages, while open-source models, especially smaller ones, lag behind. We further identify two new key challenges: (1) Under local semantic crowding, where neighboring sentences share topics and entities, models tend to follow surface cues (e.g., connectives like `however' or repeated entities) rather than the discourse role of the sentence in the surrounding context (e.g., background, explanation, outcome); and (2) A persistent gap between fluency and consistency in generated outputs, where models produce text that reads smoothly but drifts from the source facts. In addition, we observe several patterns in line with prior studies, including reliance on nearby evidence and reuse of options under uncertainty. Together, these findings highlight specific weaknesses of current LLMs and emphasize the need for multilingual, fine-grained, and position-aware evaluation, offering guidance for developing future long-context LLMs.

TL;DR: A Multilingual Granularity-Aware Long-context Benchmark