Translations:FACTS About Building Retrieval Augmented Generation-based Chatbots/68/zh: Difference between revisions

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ChipNemo ([[#bib.bib10|10]]) presents evidence for using a domain adapted language model for improving RAG’s performance on domain specific questions. They finetuned the e5-small-unsupervised model with 3,000 domain specific auto-generated samples. We tried fine-tuning e5-large embeddings model in Scout Bot. Our results did not demonstrate significant improvements. We are presently collecting high quality human-annotated data to repeat the experiments. This could be an important direction to explore in the future for our work. Another interesting technique was presented by Setty ''et. al.'' ([[#bib.bib15|15]]), in improving RAG performance using Hypothetical Document Embeddings (HYDE) technique. HyDE uses an LLM to generate a theoretical document when responding to a query and then does the similarity search with both the original question and hypothetical answer. This is a promising approach but might make the architecture complex.

ChipNemo（10）提供了使用领域适应语言模型来提高RAG在特定领域问题上的表现的证据。他们对e5-small-unsupervised模型进行了微调，使用了3,000个领域特定的自动生成样本。我们尝试在Scout Bot中微调e5-large嵌入模型。我们的结果没有显示出显著的改进。目前，我们正在收集高质量的人类注释数据以重复实验。这可能是我们未来工作中值得探索的重要方向。Setty et. al.（15）提出了另一种有趣的技术，即使用假设文档嵌入（HYDE）技术来提高RAG性能。HyDE在响应查询时使用LLM生成理论文档，然后对原始问题和假设答案进行相似性搜索。这是一种有前途的方法，但可能会使架构变得复杂。