Scholarlink

This paper evaluates a chatbot system designed for personalized business interactions using advanced Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). The system combines proprietary business data with external databases to improve contextual relevance. A comparative analysis of leading LLMs—Gemini Pro, GPT-4, Claude 2, GPT-3.5, and LLaMA 2—was conducted across benchmarks like MMLU, GSM8K, BigBench Hard, HumanEval, and DROP. Gemini Pro outperformed the others, with scores of 88.9% on MMLU, 86.3% on GSM8K, 78.1% on BigBench Hard, 73.5% on HumanEval, and 79.2% on DROP, showcasing its strength in complex reasoning and long-context retrieval. Fine-tuned with business-specific data, Gemini Pro sets a new standard for high-accuracy, scalable chatbot solutions, ideal for enterprise applications.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

RAG Chatbots: Implementing Large Language Models in Retrieval-Augmented Generations

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Kavita Patil,
  • Rohit Patil,
  • Vedanti Koyande,
  • Amaya Singh Thakur,
  • Kshitij Kadam,
  • Kavita Moholkar

摘要

This paper evaluates a chatbot system designed for personalized business interactions using advanced Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). The system combines proprietary business data with external databases to improve contextual relevance. A comparative analysis of leading LLMs—Gemini Pro, GPT-4, Claude 2, GPT-3.5, and LLaMA 2—was conducted across benchmarks like MMLU, GSM8K, BigBench Hard, HumanEval, and DROP. Gemini Pro outperformed the others, with scores of 88.9% on MMLU, 86.3% on GSM8K, 78.1% on BigBench Hard, 73.5% on HumanEval, and 79.2% on DROP, showcasing its strength in complex reasoning and long-context retrieval. Fine-tuned with business-specific data, Gemini Pro sets a new standard for high-accuracy, scalable chatbot solutions, ideal for enterprise applications.