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 support queries or research documents, are commonly used.
From the blog
IT Help Desk Automation with SiteSpeakAI
In a world that’s constantly evolving, having a robust IT help desk is no longer a choice but a necessity for businesses. But, how can you ensure that your help desk is able to respond to queries swiftly and accurately? The answer lies in automation, and one tool that is making waves in this domain is SiteSpeakAI.
Herman Schutte
Founder
Handling Unresolved Support Tickets: Escalating To Human Agents
As amazing and helpful as your ChatGPT powered custom chatbot might be, sometimes your customers or visitors still need a human touch. That's where escalating to human support comes in.
Herman Schutte
Founder