LLMs之战：对话式问答任务中的比较研究
The Battle of LLMs: A Comparative Study in Conversational QA Tasks
AI总结：
本研究比较评估了ChatGPT、GPT-4、Gemini、Mixtral和Claude在多个对话式问答语料库上的表现，通过计算和对比评估分数，揭示了各模型的整体性能及易出错领域，为改进提供了参考。
摘要：
大型语言模型因其在各种任务上的出色表现而引起了广泛关注。在这一领域中，由OpenAI开发的ChatGPT和GPT-4，以及由Google开发的Gemini，已成为早期采用者中尤为流行的模型。此外，Mistral AI发布的Mixtral和Anthropic发布的Claude也新近问世，进一步扩展了先进语言模型的版图。这些模型被视为颠覆性技术，其应用涵盖客户服务、教育、医疗保健和金融等领域。最近，Mistral进入该领域，凭借其生成创意内容的独特能力吸引了用户。理解这些用户的观点至关重要，因为他们可以提供关于这些技术在各个领域潜在优势、劣势以及整体成败的宝贵见解。本研究深入探讨了ChatGPT、GPT-4、Gemini、Mixtral和Claude在不同对话式问答语料库上生成的回答。我们精心计算了评估分数，并随后进行比较，以确定这些模型的整体性能。我们的研究指出了这些模型在回答问题时提供不准确答案的具体实例，从而揭示了它们可能容易出错的潜在领域。总之，本研究对这些最先进的语言模型进行了全面的比较和评估，阐明了它们的能力，同时强调了潜在的改进空间。
英文摘要：
Large language models have gained considerable interest for their impressive performance on various tasks. Within this domain, ChatGPT and GPT-4, developed by OpenAI, and the Gemini, developed by Google, have emerged as particularly popular among early adopters. Additionally, Mixtral by Mistral AI and Claude by Anthropic are newly released, further expanding the landscape of advanced language models. These models are viewed as disruptive technologies with applications spanning customer service, education, healthcare, and finance. More recently, Mistral has entered the scene, captivating users with its unique ability to generate creative content. Understanding the perspectives of these users is crucial, as they can offer valuable insights into the potential strengths, weaknesses, and overall success or failure of these technologies in various domains. This research delves into the responses generated by ChatGPT, GPT-4, Gemini, Mixtral and Claude across different Conversational QA corpora. Evaluation scores were meticulously computed and subsequently compared to ascertain the overall performance of these models. Our study pinpointed instances where these models provided inaccurate answers to questions, offering insights into potential areas where they might be susceptible to errors. In essence, this research provides a comprehensive comparison and evaluation of these state of-the-art language models, shedding light on their capabilities while also highlighting potential areas for improvement
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。
