What is Fine-Tuning Retrievers?
The process of adapting retrieval models to specific tasks or datasets by training them on task-relevant examples.
More about Fine-Tuning Retrievers:
Fine-Tuning Retrievers involves training retrieval models, such as bi-encoders or cross-encoders, on task-specific datasets to improve performance. Fine-tuning helps retrievers better align with the domain or context they are deployed in, enhancing relevance and accuracy.
This process is essential for optimizing systems like retrieval-augmented generation (RAG) and knowledge retrieval for specific applications.
Frequently Asked Questions
Why is fine-tuning important for retrieval models?
It adapts models to specific tasks or domains, improving the relevance of retrieved information.
What datasets are used for fine-tuning retrievers?
Datasets specific to the application domain, such as customer service queries or research documents, are commonly used.
From the blog
ChatGPT 3.5 vs ChatGPT 4 for customer support
Now that the latest version of ChatGPT 4 has been released, users of SiteSpeakAI can use the latest model for their customer support automation. I've put ChatGPT 3.5 and ChatGPT 4 to the test with some customer support questions to see how they compare.
Herman Schutte
Founder
How AI Chatbots Can Save You 100s Of Hours In Customer Support
Dive into the transformative power of AI chatbots in customer support. Learn how businesses can save significant time and enhance customer satisfaction, with a look at tools like SiteSpeakAI.
Herman Schutte
Founder