Exploring Context Strategies in LLM s for Discourse-Aware Machine Translation (2025)

Abstract: While large language models (LLMs) excel at machine translation (MT), the impact of how LLMs utilize different forms of contextual information on discourse-level phenomena remains underexplored. We systematically investigate how different forms of context such as prior source sentences, models’ generated hypotheses, and reference translations influence standard MT metrics and specific discourse phenomena (formality, pronoun selection, and lexical cohesion). Evaluating multiple LLMs across multiple domains and language pairs, our findings consistently show that context boosts both translation and discourse-specific performance. Notably, the context strategy of combining source text with the model’s own prior hypotheses effectively improves discourse consistency without gold references, demonstrating effective use of model’s own imperfect generations as diverse contextual cues.

## introduction large language models (llms) have demonstrated remarkable machine translation (mt) capabilities, often producing outputs close to human quality @xcite @xcite . however, accurately rendering discourse-level phenomena such as appropriate formality or consistent lexical cohesion has been a persistent challenge in mt @xcite @xcite . discourse phenomenon is vital for producing translations that are not only fluent but also preserve nuanced meaning across segments. while llms' extensive context windows offer a promising avenue for improving discourse-aware translation by moving beyond sentence-level limitations @xcite , how to make most of this potential for improving discourse accuracy remains an issue @xcite . in addition, much of the current llm-mt research, while demonstrating impressive gains, often focuses on improving overall translation quality measured by standard metrics like bleu @xcite or comet @xcite , with less emphasis on a granular analysis of contextstrategy effectiveness for specific discourse phenomena @xcite . this paper aims to bridge this gap by analyzing how llms leverage diverse forms of context to improve discourse-aware translation. we investigate the impact of various context strategies, such as using source text, model-generated hypotheses, reference translations, and combinations thereof, on the performances of two leading llms, gpt-4o @xcite and gemma 2-27b @xcite . to ensure the potential generalizability of our findings, our evaluation covers multiple language pairs (english-to-japanese, english-to-french, and english-to-german) and two distinct domains: monologues from the ted talks corpus, dialogue from an curated opensubtitles subset. also, we move beyond conventional mt evaluation metrics by employing the muda framework @xcite to assess discourse phenomena such as formality, pronoun, and lexical cohesion. our key findings reveal that: (1) llms utilize context to improve performance compared to a no-context baseline, across both translation and discourse metrics, confirming and extending findings from nmt @xcite ; (2) even without gold references as context, llms can effectively leverage source-side context and their own prior hypotheses indicating a capacity for selfsupervision that enhances discourse consistency. ## methodology this section outlines the methodology used to investigate how context utilization affects discourse phenomena in llm-based machine translation. formally, given a sequence of cohesive source sentences @xmath0, ..., s n-1 }, the task is to generate a hypothesis translation h n for the final source sentence s n , conditioned on s n and relevant context c n . this can be represented as generating h n given (s n , c n ). ## experiments this section outlines the setup used to evaluate the impact of the various context strategies. we describe the dataset and evaluation metrics, followed by the models and experimental configurations. ## results and analysis ## conclusion this work evaluated how llms leverage various contextual cues for discourse-aware machine translation across diverse settings. we found that all explored context strategies improved both standard mt metrics and discourse phenomena like formality and pronoun selection compared to no-context baselines. notably, combining prior source text with model-generated hypotheses proved to be effective for improving discourse consistency without gold references, hinting at potential for selfsupervision. future work includes extending this analysis to more typologically diverse languages and a wider range of discourse phenomena.