ChatGPT与DeepSeek在关键NLP任务上的比较评估：优势、劣势及特定领域性能
Comparative Evaluation of ChatGPT and DeepSeek Across Key NLP Tasks: Strengths, Weaknesses, and Domain-Specific Performance
AI总结：
本研究在情感分析、主题分类、文本摘要、机器翻译和文本蕴含五个关键NLP任务上全面评估了ChatGPT和DeepSeek，发现DeepSeek在分类稳定性和逻辑推理上占优，而ChatGPT更擅长需细微理解和灵活性的任务。
摘要：
大型语言模型（LLMs）在自然语言处理（NLP）任务中的日益广泛应用，引发了人们评估其在不同应用中有效性的浓厚兴趣。尽管ChatGPT和DeepSeek等模型在许多NLP领域显示出强劲的结果，但仍需进行全面评估以了解其优势、劣势及特定领域能力。这一点至关重要，因为这些模型被应用于从情感分析到文本蕴含和翻译等更细微任务的各种任务。本研究旨在评估ChatGPT和DeepSeek在五个关键NLP任务上的表现：情感分析、主题分类、文本摘要、机器翻译和文本蕴含。采用结构化实验协议以确保公平性并最小化变异性。两个模型均使用相同的中性提示进行测试，并在每个任务的两个基准数据集上进行评估，涵盖新闻、评论以及正式/非正式文本等领域。结果表明，DeepSeek在分类稳定性和逻辑推理方面表现突出，而ChatGPT在需要细微理解和灵活性的任务中表现更好。这些发现为根据任务要求选择合适的LLM提供了有价值的见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
