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Context Reranking reflects the efficiency of the reranking process applied to the original context in generating the new renaked context used to answer a given question. This operator assess the degree to which the reranked context enhances the relevance, coherence, and informativeness with respect to the provided question. Columns required:
  • question: The question asked by the user
  • context: Information retrieved to answer the question
  • reranked_context: The context generated from the original context using the reranking algorithm

How to use it?

By default, we are using GPT 3.5 Turbo for evaluations. If you want to use a different model, check out this tutorial.
Sample Response:
A higher context reranking score reflects that reranked context is more efficient in answering the question as compared to the original context.
The original context was better suited to answer the question: “How to make red sauce pasta?” as the steps mentioned were in logical order. The reranked context has changed the ordering of the steps breaking the logical flow of order, making it less efficient compared to the original context

How it works?

We evaluate context reranking by determining which of the following three cases apply for the given task data:
  • The reranking of the original context is highly effective in generating the reranked context for the given question.
  • The reranking of the original context is somewhat effective in generating the reranked context for the given question.
  • The reranking of the original context is not very effective in generating the reranked context for the given question.

Tutorial

Open this tutorial in GitHub

Have Questions?

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