Over the years, scalar MT metrics have advanced rapidly on benchmarks. Yet they remain black boxes, offering little insight into their decisions and sometimes degrading under out-of-distribution inputs. We introduce Remedy-R, a reasoning-driven generative MT metric trained with reinforcement learning from pairwise translation preferences, without requiring error-span annotations or distillation from closed LLMs. Unlike scalar MT metrics that only outputs translation quality scores, Remedy-R produces step-by-step analyses of accuracy, fluency, and completeness, enabling more interpretable assessments. With only 60K pairwise training samples across two language pairs, Remedy-R remains competitive with top scalar metrics and GPT-4-based judges on WMT22–24 metric benchmarks, generalizes to other languages, and shows strong robustness on OOD stress tests. Moreover, Remedy-R generates self-reflective feedback that can be reused for translation refinement. We validate the faithfulness of such feedback with GPT-4 and show that a simple evaluate–revise pipeline leveraging Remedy-R’s analyses consistently improves translation quality across diverse models without any task-specific tuning.

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.

Building open-domain dialogue systems capable of rich human-like conversational ability is one of the fundamental challenges in language generation. However, even with recent advancements in the field, existing open-domain generative models fail to capture and utilize external knowledge, leading to repetitive or generic responses to unseen utterances. Current work on knowledge-grounded dialogue generation primarily focuses on persona incorporation or searching a fact-based structured knowledge source such as Wikipedia. Our method takes a broader and simpler approach, which aims to improve the raw conversation ability of the system by mimicking the human response behavior through casual interactions found on social media. Utilizing a joint retriever-generator setup, the model queries a large set of filtered comment data from Reddit to act as additional context for the seq2seq generator. Automatic and human evaluations on open-domain dialogue datasets demonstrate the effectiveness of our approach.