Are Large Language Models State-of-the-art Quality Estimators for Machine Translation of User-generated Content?

• URL: http://arxiv.org/abs/2410.06338v1
• Date: Tue, 8 Oct 2024 20:16:59 GMT
• Title: Are Large Language Models State-of-the-art Quality Estimators for Machine Translation of User-generated Content?
• Authors: Shenbin Qian, Constantin Orăsan, Diptesh Kanojia, Félix do Carmo,
• Abstract summary: This paper investigates whether large language models (LLMs) are state-of-the-art quality estimators for machine translation of user-generated content (UGC) We employ an existing emotion-related dataset with human-annotated errors and calculate quality evaluation scores based on the Multi-dimensional Quality Metrics.
• Score: 6.213698466889738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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