Towards Large Language Model driven Reference-less Translation Evaluation for English and Indian Languages
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929302015049728 |
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| author | Mujadia, Vandan Mishra, Pruthwik Ahsan, Arafat Sharma, Dipti Misra |
| author_facet | Mujadia, Vandan Mishra, Pruthwik Ahsan, Arafat Sharma, Dipti Misra |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02512 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Large Language Model driven Reference-less Translation Evaluation for English and Indian Languages Mujadia, Vandan Mishra, Pruthwik Ahsan, Arafat Sharma, Dipti Misra Computation and Language 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. |
| title | Towards Large Language Model driven Reference-less Translation Evaluation for English and Indian Languages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.02512 |