@article{ataee-etal-2026-chain,
    title = "Chain-of-Thought Reasoning Improves Context-Aware Translation with Large Language Models",
    author = "Ataee, Shabnam  and
      Huart, Hugo  and
      Popescu-Belis, Andrei",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.298/",
    pages = "3725--3741",
    abstract = "This paper assesses the ability of large language models (LLMs) to translate texts that include inter-sentential dependencies. We use the English-French DiscEvalMT benchmark (Bawden et al., 2018) with pairs of sentences containing translation challenges for pronominal anaphora and lexical cohesion. We evaluate 12 LLMs from the DeepSeek-R1, GPT, Llama, Mistral and Phi families on two tasks: (1) distinguish a correct translation from a wrong but plausible one; and (2) generate a correct translation. We compare prompts that encourage chain-of-thought reasoning with those that do not. The best models take advantage of reasoning and reach about 90{\%} accuracy on the first task and COMET scores of about 92{\%} on the second task, with GPT-4, GPT-4o and Phi standing out. Moreover, we observe a ``wise get wiser'' effect: the improvements through reasoning are larger for models that already perform well without reasoning."
}