@inproceedings{hengle-etal-2026-llms,
    title = "Can {LLM}s reason over extended multilingual contexts? Towards long-context evaluation beyond retrieval over haystacks",
    author = "Hengle, Amey  and
      Bajpai, Prasoon  and
      Dan, Soham  and
      Chakraborty, Tanmoy",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.290/",
    pages = "6128--6152",
    ISBN = "979-8-89176-380-7",
    abstract = "Existing multilingual long-context benchmarks, often based on the popular needle-in-a-haystack test, primarily evaluate a model{'}s ability to locate specific information buried within irrelevant texts. However, such a retrieval-centric approach is myopic and inherently limited, as successful recall alone does not indicate a model{'}s capacity to reason over extended contexts. Moreover, these benchmarks are susceptible to data leakage, short-circuiting, and risk making the evaluation a priori identifiable. To address these limitations, we introduce MLRBench, a new synthetic benchmark for multilingual long-context reasoning. Unlike existing benchmarks, MLRBench goes beyond surface-level retrieval by including bAbI-style tasks that test multi-hop inference, aggregation, and epistemic reasoning. Spanning seven languages, we design MLRBench to be parallel, resistant to leakage, and scalable to arbitrary context lengths. Our extensive experiments with an open-weight large language model (LLM) reveal a pronounced gap between high- and low-resource languages, particularly for tasks requiring the model to aggregate multiple facts or predict the absence of information. We also find that, in multilingual settings, LLMs effectively utilize less than 30{\%} of their claimed context length. Although off-the-shelf Retrieval-Augmented Generation helps alleviate this to a certain extent, it does not solve the long-context problem. We open-source MLRBench to enable future research in the improved evaluation and training of multilingual LLMs."
}