Evaluating Prompt Scope and Demonstration Similarity in Local LLM Machine Translation
This paper investigates prompt scope and demonstration selection as experimental variables in local LLM machine translation. Comparing three local instruction-tuned LLMs against dedicated MT baselines, the study finds that dedicated systems remain strongest, especially for Germanic languages. Few-shot prompting helps some models but hurts others, and family-scope prompting exposes failures in smaller models. The results call for evaluating LLM translation beyond language pairs and metrics, considering prompt scope, retrieval strategy, and multi-target compliance.
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[Submitted on 28 Jul 2026]
Title:Evaluating Prompt Scope and Demonstration Similarity in Local LLM Machine Translation
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Abstract:Large language models (LLMs) are increasingly used as general-purpose translation systems, but their behavior is usually evaluated under a single prompt shape: translate one source sentence into one target language. In practice, users may ask for one target language, for several related languages at once, or for translations conditioned on examples. This paper studies prompt scope and demonstration selection as experimental variables for local LLM machine translation. We evaluate English-to-Romance and English-to-Germanic translation on the full FLORES devtest split for nine official European Union languages. We compare three local instruction-tuned LLMs, llama3.2:3b, mistral:latest, and qwen2.5:14b, against dedicated MT baselines from OPUS-MT and NLLB-200. We test zero-shot prompting and k=5 few-shot prompting with random, lexical-similarity, and embedding-similarity demonstration selection. We also compare single-target prompts with JSON-formatted family-scope prompts that request all languages in a family at once. Results show that dedicated MT systems remain strongest overall, especially for Germanic languages. Few-shot prompting helps mistral:latest and qwen2.5:14b, but hurts llama3.2:3b; embedding retrieval is best on average for the stronger LLMs, but its advantage over random and lexical examples is modest. Family-scope prompting is feasible for stronger local LLMs but exposes structured-output failures in smaller models. These findings motivate evaluating LLM translation not only by language pair and metric, but also by prompt scope, retrieval strategy, and multi-target compliance.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2607.26286 [cs.CL]
(or arXiv:2607.26286v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.26286
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mihael Arcan [view email] [v1] Tue, 28 Jul 2026 21:26:36 UTC (18 KB)
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