伯克利博士论文 | 从领域适配到部署与可解释：Berkeley博士论文解析大语言模型真实落地
Merged summary
TL;DR - A 2026 UC Berkeley dissertation presents domain adaptation as full-stack LLM systems engineering spanning knowledge ingestion, RAG, fine-tuning, deployment, tools, evaluation, and interpretability.
- Agricultural QA accuracy rose from 75% with base GPT-4 to 86% when fine-tuning and RAG were combined.
- A hierarchical device-edge-cloud architecture routes queries based on connectivity, latency, privacy, load, and difficulty.
- The Copilot framework separates offline knowledge-resource construction from an online, plugin-based runtime.
- QA-Emb creates interpretable embeddings from answers to natural-language yes/no questions, matching BERT on fMRI prediction while trailing LLaMA representations.
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伯克利博士论文 | 从领域适配到部署与可解释：Berkeley博士论文解析大语言模型真实落地
TL;DR - A 2026 UC Berkeley dissertation presents domain adaptation as full-stack LLM systems engineering spanning knowledge ingestion, RAG, fine-tuning, deployment, tools, evaluation, and interpretability.
- Agricultural QA accuracy rose from 75% with base GPT-4 to 86% when fine-tuning and RAG were combined.
- A hierarchical device-edge-cloud architecture routes queries based on connectivity, latency, privacy, load, and difficulty.
- The Copilot framework separates offline knowledge-resource construction from an online, plugin-based runtime.
- QA-Emb creates interpretable embeddings from answers to natural-language yes/no questions, matching BERT on fMRI prediction while trailing LLaMA representations.