A Komi-Yazva--Russian Parallel Corpus and Evaluation Protocol for Zero- and Few-Shot LLM Translation

• URL: http://arxiv.org/abs/2606.06420v1
• Date: Thu, 04 Jun 2026 17:26:38 GMT
• Title: A Komi-Yazva--Russian Parallel Corpus and Evaluation Protocol for Zero- and Few-Shot LLM Translation
• Abstract summary: We present the first Komi-Yazva--Russian parallel corpus together with an explicit evaluation protocol.<n>The dataset contains 457 aligned sentence pairs from 74 narrative texts.<n>We use this setup to compare modern large language models on Komi-Yazva-to-Russian translation under severe parallel-data scarcity.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the first Komi-Yazva--Russian parallel corpus together with an explicit evaluation protocol for studying LLM translation in an endangered, extremely low-resource setting. The dataset contains 457 aligned sentence pairs from 74 narrative texts and is accompanied by documented provenance, sentence-level alignment, and story identifiers that enable leakage-aware evaluation. We use this setup to compare modern large language models on Komi-Yazva-to-Russian translation under severe parallel-data scarcity in zero-shot and retrieval-based few-shot regimes. The protocol includes story-level cross-validation, deterministic retrieval for few-shot prompting, strict validation of generated outputs, complementary reference-based and judge-based metrics, and story-level uncertainty estimates. Across models, LLMs produce non-trivial translations, but performance varies strongly by model family and prompting regime. Retrieval-based few-shot prompting consistently improves over zero-shot prompting, while gains beyond a small retrieved context remain limited. The results show that evaluative conclusions in this setting depend materially on metric choice and failure handling, so the paper frames the corpus as both a dataset contribution and a reproducible evaluation testbed for endangered-language machine translation.

Related papers

• When Simpler Is Better: Evaluating Translation Pipelines for Medieval Latin Manuscripts [3.3982074915384874]
  We evaluate the full image-to-translation pipeline on medieval Latin manuscripts.<n> domain-specific Optical Character Recognition (OCR) models reduce character error rate by up to 4.3$times$ compared to general-purpose VLMs.<n>These findings offer both a new benchmark and practical guidance for deploying translation systems in low-resource historical settings.
  arXiv  Detail & Related papers  (2026-07-04T11:58:52Z)
• Multilingual Coreference Resolution via Cycle-Consistent Machine Translation [20.187814511700243]
  Coreference resolution is a core NLP task, having a broad range of downstream applications, e.g.machine translation, question answering, document summarization, etc.<n>We propose a novel coreference resolution pipeline that harnesses machine translation (MT) from English to a target low-resource language.<n>Experiments on four low-resource languages show that our pipeline brings significant performance gains in coreference resolution.
  arXiv  Detail & Related papers  (2026-06-03T21:06:55Z)
• LLMs as annotators of credibility assessment in Danish asylum decisions: evaluating classification performance and errors beyond aggregated metrics [21.604030114864642]
  Off-the-shelf large language models (LLMs) are increasingly used to automate text annotation.<n>We introduce RAB-Cred, a Danish text classification dataset featuring high-quality, expert annotations and valuable metadata.<n>We benchmark 21 open-weight models and 30 system-user prompt combinations for this task, and systematically evaluate the effect of model and prompt choice for zero-shot and few-shot classification.
  arXiv  Detail & Related papers  (2026-05-13T12:07:47Z)
• XQ-MEval: A Dataset with Cross-lingual Parallel Quality for Benchmarking Translation Metrics [64.77152900881724]
  We propose XQ-MEval, a semi-automatically built dataset covering nine translation directions.<n>We inject MQM-defined errors into gold translations automatically, filter them by native speakers for reliability, and merge errors to generate pseudo translations with controllable quality.<n>Using XQ-MEval, our experiments on nine representative metrics reveal the inconsistency between averaging and human judgment.
  arXiv  Detail & Related papers  (2026-04-16T12:27:10Z)
• Evaluating Extremely Low-Resource Machine Translation: A Comparative Study of ChrF++ and BLEU Metrics [69.2321983942375]
  This work presents a comparative analysis of BLEU, an n-gram-based metric, and ChrF++, a character-based metric, for MT evaluation in ELRL settings.<n>We examine how each metric responds to translation artifacts, including hallucinations, repetition, source-text copying, and diacritic (textitmatra) variations across three ELRLs: Magahi, Bhojpuri, and Chhattisgarhi.<n>While recent work often relies solely on ChrF++, our findings show that BLEU, despite its lower absolute scores, provides complementary lexical-precision insights that improve interpretability.
  arXiv  Detail & Related papers  (2026-02-19T14:56:42Z)
• LLM-Based Evaluation of Low-Resource Machine Translation: A Reference-less Dialect Guided Approach with a Refined Sylheti-English Benchmark [1.3927943269211591]
  We propose a comprehensive framework that enhances Large Language Models (LLMs)-based machine translation evaluation.<n>We extend the ONUBAD dataset by incorporating Sylheti-English sentence pairs, corresponding machine translations, and Direct Assessment (DA) scores annotated by native speakers.<n>Our evaluation shows that the proposed pipeline consistently outperforms existing methods, achieving the highest gain of +0.1083 in Spearman correlation.
  arXiv  Detail & Related papers  (2025-05-18T07:24:13Z)
• Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answering [68.3400058037817]
  We introduce TREQA (Translation Evaluation via Question-Answering), a framework that extrinsically evaluates translation quality.<n>We show that TREQA is competitive with and, in some cases, outperforms state-of-the-art neural and LLM-based metrics in ranking alternative paragraph-level translations.
  arXiv  Detail & Related papers  (2025-04-10T09:24:54Z)
• When LLMs Struggle: Reference-less Translation Evaluation for Low-resource Languages [9.138590152838754]
  Segment-level quality estimation (QE) is a challenging cross-lingual language understanding task.<n>We comprehensively evaluate large language models (LLMs) in zero/few-shot scenarios.<n>Our results indicate that prompt-based approaches are outperformed by the encoder-based fine-tuned QE models.
  arXiv  Detail & Related papers  (2025-01-08T12:54:05Z)
• Language Models and Cycle Consistency for Self-Reflective Machine Translation [1.79487674052027]
  We generate multiple translation candidates from a source language A to a target language B, and subsequently translate these candidates back to the original language A. By evaluating the cycle consistency between the original and back-translated sentences using metrics such as token-level precision and accuracy, we implicitly estimate the translation quality in language B. For each source sentence, we identify the translation candidate with optimal cycle consistency with the original sentence as the final answer.
  arXiv  Detail & Related papers  (2024-11-05T04:01:41Z)
• Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles [53.92189950211852]
  Large language models have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-context learning. In this paper, we investigate the factors contributing to this gap and find that this gap can largely be closed (for about 70%) by matching the writing styles of the target corpus.
  arXiv  Detail & Related papers  (2023-11-04T03:18:45Z)
• Understanding Translationese in Cross-Lingual Summarization [106.69566000567598]
  Cross-lingual summarization (MS) aims at generating a concise summary in a different target language. To collect large-scale CLS data, existing datasets typically involve translation in their creation. In this paper, we first confirm that different approaches of constructing CLS datasets will lead to different degrees of translationese.
  arXiv  Detail & Related papers  (2022-12-14T13:41:49Z)
• On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation [55.02832094101173]
  Evaluation of cross-lingual encoders is usually performed either via zero-shot cross-lingual transfer in supervised downstream tasks or via unsupervised cross-lingual similarity. This paper concerns ourselves with reference-free machine translation (MT) evaluation where we directly compare source texts to (sometimes low-quality) system translations. We systematically investigate a range of metrics based on state-of-the-art cross-lingual semantic representations obtained with pretrained M-BERT and LASER. We find that they perform poorly as semantic encoders for reference-free MT evaluation and identify their two key limitations.
  arXiv  Detail & Related papers  (2020-05-03T22:10:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.