Towards Large Language Model driven Reference-less Translation Evaluation for English and Indian Languages
This work addresses the challenge of evaluating translation quality without reference texts, specifically for Indian languages, though it is incremental as it applies existing LLM techniques to a new domain.
This paper tackles the problem of reference-less translation evaluation for English and Indian languages by using large language models (LLMs) to mimic human assessments, finding that an LLM-based evaluator (LLaMA-2-13B) achieves comparable or higher correlation with human judgments compared to existing methods.
With the primary focus on evaluating the effectiveness of large language models for automatic reference-less translation assessment, this work presents our experiments on mimicking human direct assessment to evaluate the quality of translations in English and Indian languages. We constructed a translation evaluation task where we performed zero-shot learning, in-context example-driven learning, and fine-tuning of large language models to provide a score out of 100, where 100 represents a perfect translation and 1 represents a poor translation. We compared the performance of our trained systems with existing methods such as COMET, BERT-Scorer, and LABSE, and found that the LLM-based evaluator (LLaMA-2-13B) achieves a comparable or higher overall correlation with human judgments for the considered Indian language pairs.