Are Large Language Models State-of-the-art Quality Estimators for Machine Translation of User-generated Content?
<|reference_start|>Are Large Language Models State-of-the-art Quality Estimators for Machine Translation of User-generated Content?: This paper investigates whether large language models (LLMs) are state-of-the-art quality estimators for machine translation of user-generated content (UGC) that contains emotional expressions, without the use of reference translations. To achieve this, we employ an existing emotion-related dataset with human-annotated errors and calculate quality evaluation scores based on the Multi-dimensional Quality Metrics. We compare the accuracy of several LLMs with that of our fine-tuned baseline models, under in-context learning and parameter-efficient fine-tuning (PEFT) scenarios. We find that PEFT of LLMs leads to better performance in score prediction with human interpretable explanations than fine-tuned models. However, a manual analysis of LLM outputs reveals that they still have problems such as refusal to reply to a prompt and unstable output while evaluating machine translation of UGC.<|reference_end|>
@article{qian2024are,
    title={Are Large Language Models State-of-the-art Quality Estimators for
  Machine Translation of User-generated Content?},
    author={Shenbin Qian, Constantin Oru{a}san, Diptesh Kanojia, F'elix do Carmo},
    journal={arXiv preprint arXiv:2410.06338},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06338},
    primaryClass={cs.CL}
}