When LLMs Struggle: Reference-less Translation Evaluation for Low-resource Languages

• URL: http://arxiv.org/abs/2501.04473v1
• Date: Wed, 08 Jan 2025 12:54:05 GMT
• Title: When LLMs Struggle: Reference-less Translation Evaluation for Low-resource Languages
• Authors: Archchana Sindhujan, Diptesh Kanojia, Constantin Orasan, Shenbin Qian,
• Abstract summary: Segment-level quality estimation (QE) is a challenging cross-lingual language understanding task. We comprehensively evaluate large language models (LLMs) in zero/few-shot scenarios. Our results indicate that prompt-based approaches are outperformed by the encoder-based fine-tuned QE models.
• Score: 9.138590152838754
• License:
• Abstract: This paper investigates the reference-less evaluation of machine translation for low-resource language pairs, known as quality estimation (QE). Segment-level QE is a challenging cross-lingual language understanding task that provides a quality score (0-100) to the translated output. We comprehensively evaluate large language models (LLMs) in zero/few-shot scenarios and perform instruction fine-tuning using a novel prompt based on annotation guidelines. Our results indicate that prompt-based approaches are outperformed by the encoder-based fine-tuned QE models. Our error analysis reveals tokenization issues, along with errors due to transliteration and named entities, and argues for refinement in LLM pre-training for cross-lingual tasks. We release the data, and models trained publicly for further research.

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