通过情感与语义分析评估LLM与谷歌翻译对选定印度语言翻译的质量
An evaluation of LLMs and Google Translate for translation of selected Indian languages via sentiment and semantic analyses
AI总结：
本研究通过情感与语义分析评估LLM和谷歌翻译对梵语、泰卢固语、印地语文本的翻译质量，发现GPT模型在保持情感极性和语义方面优于谷歌翻译，但在隐喻和哲学语境中仍存在挑战。
摘要：
大型语言模型（LLMs）在语言翻译方面表现突出，包括低资源语言。然而，针对LLM（包括Gemini、GPT和Google Translate）生成翻译质量的评估研究仍然有限。本研究通过语义和情感分析评估选定的LLM对印度语言（包括梵语、泰卢固语和印地语）的翻译质量，以弥补这一不足。我们选取了由专家精心翻译的著名文本（《薄伽梵歌》、《塔马斯》和《大征程》），使用LLM生成其英文翻译，并与选定的专家（人工）翻译进行比较。我们的调查表明，尽管LLM在翻译准确性方面取得了显著进展，但在保持情感和语义完整性方面仍面临挑战，尤其是在《薄伽梵歌》等隐喻性和哲学性文本中。情感分析显示，与人工（专家）翻译相比，GPT模型在保持给定文本情感极性方面表现更好。结果表明，与Google Translate相比，GPT模型通常在保持情感和语义方面表现更优。本研究有助于为大型语言模型开发准确且具有文化敏感性的翻译系统。
英文摘要：
Large Language models (LLMs) have been prominent for language translation, including low-resource languages. There has been limited study on the assessment of the quality of translations generated by LLMs, including Gemini, GPT, and Google Translate. This study addresses this limitation by using semantic and sentiment analysis of selected LLMs for Indian languages, including Sanskrit, Telugu and Hindi. We select prominent texts (Bhagavad Gita, Tamas and Maha Prasthanam ) that have been well translated by experts and use LLMs to generate their translations into English, and provide a comparison with selected expert (human) translations. Our investigation revealed that while LLMs have made significant progress in translation accuracy, challenges remain in preserving sentiment and semantic integrity, especially in metaphorical and philosophical contexts for texts such as the Bhagavad Gita. The sentiment analysis revealed that GPT models are better at preserving the sentiment polarity for the given texts when compared to human (expert) translation. The results revealed that GPT models are generally better at maintaining the sentiment and semantics when compared to Google Translate. This study could help in the development of accurate and culturally sensitive translation systems for large language models.
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