对比大模型与谷歌翻译在印度语翻译中的表现，发现GPT更保情感和语义。
An evaluation of LLMs and Google Translate for translation of selected Indian languages via sentiment and semantic analyses
- 用语义与情感分析评估GPT、Gemini等对梵语、泰卢固语等的翻译效果。
- GPT在哲学文本中更准确保留情感极性，优于谷歌翻译。
- 适合关注低资源语言翻译质量与文化适配的研究者参考。
大型语言模型（LLMs）在语言翻译中表现突出，尤其在低资源语言领域。然而，针对LLMs生成翻译质量的系统评估仍有限，包括Gemini、GPT和Google Translate。本研究通过语义与情感分析，评估了这些模型对梵语、泰卢固语和印地语的翻译表现，选取《薄伽梵歌》《暗》《大迁徙》等经典文本作为测试样本，其专家翻译版本作为基准。结果表明，尽管LLMs在翻译准确性上进步显著，但在哲理与隐喻性文本中仍难以保持情感与语义完整性。情感分析显示，相比人工翻译，GPT模型在保持情感极性方面表现更优；整体而言，GPT在语义与情感一致性上优于Google Translate。该研究有助于构建更精准、文化敏感的LLM翻译系统。
原文摘要 · Abstract (English)
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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