对比ChatGPT与DeepSeek在五大NLP任务中的表现，指导模型选型。
Comparative Evaluation of ChatGPT and DeepSeek Across Key NLP Tasks: Strengths, Weaknesses, and Domain-Specific Performance
- 统一测试流程，用相同提示评估两个模型在五类任务上的表现。
- DeepSeek在分类与推理任务中更稳定，ChatGPT在理解灵活性上更强。
- 结果帮助用户根据任务需求选择更适合的模型。
大型语言模型（LLM）在自然语言处理（NLP）任务中的广泛应用引发了对其性能的广泛关注。尽管ChatGPT与DeepSeek在多个NLP领域已表现出色，但仍需全面评估其优劣势及领域特异性能力。本研究针对情感分析、主题分类、文本摘要、机器翻译和文本蕴含五个关键任务展开比较。采用标准化实验协议，使用一致且中性的提示，并在每项任务上基于两个基准数据集进行评估，涵盖新闻、评论及正式/非正式文本等不同领域。结果显示，DeepSeek在分类稳定性与逻辑推理方面表现更佳，而ChatGPT在需要细微理解与灵活性的任务中更具优势。这些发现为依据任务需求选择合适的大型语言模型提供了重要参考。
原文摘要 · Abstract (English)
The increasing use of large language models (LLMs) in natural language processing (NLP) tasks has sparked significant interest in evaluating their effectiveness across diverse applications. While models like ChatGPT and DeepSeek have shown strong results in many NLP domains, a comprehensive evaluation is needed to understand their strengths, weaknesses, and domain-specific abilities. This is critical as these models are applied to various tasks, from sentiment analysis to more nuanced tasks like textual entailment and translation. This study aims to evaluate ChatGPT and DeepSeek across five key NLP tasks: sentiment analysis, topic classification, text summarization, machine translation, and textual entailment. A structured experimental protocol is used to ensure fairness and minimize variability. Both models are tested with identical, neutral prompts and evaluated on two benchmark datasets per task, covering domains like news, reviews, and formal/informal texts. The results show that DeepSeek excels in classification stability and logical reasoning, while ChatGPT performs better in tasks requiring nuanced understanding and flexibility. These findings provide valuable insights for selecting the appropriate LLM based on task requirements.
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