轻量级多领域聊天机器人，用极少数据和硬件快速训练
CARE: A QLoRA-Fine Tuned Multi-Domain Chatbot With Fast Learning On Minimal Hardware
- 基于Phi3.5-mini用极少量数据微调，支持三领域问答
- 在医疗等基准测试中表现良好，可提供初步诊断建议
- 可在手机端运行，适合资源受限场景使用
大型语言模型在特定领域数据上微调后展现出优秀的问答能力。然而，传统微调需大量训练时间和计算资源。本文提出CARE（客户辅助与响应引擎），一个基于Phi3.5-mini的轻量级模型，仅需极少数据和最低配置硬件即可完成训练，主要用于电信、医疗和银行三个领域的客户问题处理。在电信与银行领域，解决用户常见问题；在医疗领域，提供基础诊断与就医建议，供用户就诊前参考。由于基于Phi3.5-mini，CARE可在移动设备上运行，提升实用性。研究还表明，CARE在多个医疗基准测试中表现良好，具备提供基础医疗建议的能力。
原文摘要 · Abstract (English)
Large Language models have demonstrated excellent domain-specific question-answering capabilities when finetuned with a particular dataset of that specific domain. However, fine-tuning the models requires a significant amount of training time and a considerable amount of hardware. In this work, we propose CARE (Customer Assistance and Response Engine), a lightweight model made by fine-tuning Phi3.5-mini on very minimal hardware and data, designed to handle queries primarily across three domains: telecommunications support, medical support, and banking support. For telecommunications and banking, the chatbot addresses issues and problems faced by customers regularly in the above-mentioned domains. In the medical domain, CARE provides preliminary support by offering basic diagnoses and medical suggestions that a user might take before consulting a healthcare professional. Since CARE is built on Phi3.5-mini, it can be used even on mobile devices, increasing its usability. Our research also shows that CARE performs relatively well on various medical benchmarks, indicating that it can be used to make basic medical suggestions.
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