Comparing two outputs

This evaluation requires an evaluator, a dataset of inputs, and two or more LLMs, chains, or agents to compare. The evaluation aggregates the results to determine the preferred model.

The evaluation process involves several steps:

1. Create the evaluator: Load the evaluator using the load_evaluator() function, specifying the type of evaluator (in this case, pairwise_string).
2. Select the dataset: Load a dataset of inputs using the load_dataset() function.
3. Define models to compare: Initialize the LLMs, chains, or agents to compare using the necessary configurations. This involves initializing the language model and any additional tools or agents required.
4. Generate responses: Generate outputs for each of the models before evaluating them. This is typically done in batches to improve efficiency.
5. Evaluate pairs: Evaluate the results by comparing the outputs of different models for each input. This is often done using a random selection...