KIKIS at WMT 2025 General Translation Task (2025)

Abstract: We participated in the constrained English–Japanese track of the WMT 2025 General Machine Translation Task.Our system collected the outputs produced by multiple subsystems, each of which consisted of LLM-based translation and reranking models configured differently (e.g., prompting strategies and context sizes), and reranked those outputs.Each subsystem generated multiple segment-level candidates and iteratively selected the most probable one to construct the document translation.We then reranked the document-level outputs from all subsystems to obtain the final translation.For reranking, we adopted a text-based LLM reranking approach with a reasoning model to take long contexts into account.Additionally, we built a bilingual dictionary on the fly from parallel corpora to make the system more robust to rare words.

## introduction this paper describes kikis's submission to the wmt 2025 general machine translation shared task @xcite . we participated in the constrained track for the english-japanese (@xmath0. given limited computational resources and the rapid pace of open-source llm releases, we aimed to build a system that produced high-quality translations without additional training. in particular, we aimed to detect and correct residual errors, such as mismatched numbers or dates, missing key terms, and unnatural phrasing, in otherwise strong translations produced by llmbased mt systems. to this end, we adopted a multi-stage llm-based reranking pipeline that selected the best translation from candidate outputs. this paper provides a detailed description of our submitted system. we also report post-evaluation results that demonstrate the effectiveness of each component of our system. ## system overview figure 1 provides an overview of our system. our system consisted of four translation subsystems and a document reranker. we first aggregated outputs from the four subsystems, which differed in configuration (e.g., prompting strategies and context sizes). we then performed document-level reranking to select the best translation from the combined candidates. within each subsystem, the primary translation module generated multiple segmentlevel hypotheses. the segment reranker iteratively filtered the candidate set via a tournament-style process to select the most plausible hypothesis. algorithm 1 shows the pseudocode for the subsystem. below, we describe three components: the primary translation module ( § 3), the segment reranker ( § 4), and the document reranker ( § 5). ## primary translation module we used plamo-2-translate @xcite as our base model. plamo-2-translate is an llmbased translation system with a hybrid architecture that combined mamba (gu and dao, 2024) and transformers @xcite . with this model, we generated 32 hypotheses for each source segment. the decoding hyperparameters are listed in ## document reranker we reranked document-level translation candidates generated by n sub subsystems using qwen3-8b. we performed pairwise comparisons over all ordered pairs of candidates and selected the most plausible translation. as in the segment reranking, each pair was evaluated in both input orders to deal with positional bias. ## post-evaluation we conducted a post-evaluation to assess the contribution of each component in our system. ## conclusion we described the kikis submission to the wmt 2025 general machine translation shared task. we participated in the constrained track for the english-japanese (@xmath1. our system consisted of four translation subsystems and a document reranker. each subsystem combined an mt model with an llm-based segment reranker. we aggregated the outputs from the four subsystems and then applied document-level reranking to select the final translation.