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Update README.md - Add missing "Y" to "ou" #230

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Feb 9, 2024
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Update README.md - Add missing "Y" to "ou" #230

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -1256,7 +1256,7 @@ For example:

For some annotators, e.g. `alpaca_eval_cot_gpt4_turbo_fn` we use chan of thought reasoning to make the models preferences more interpretable. Those can then be found under `concise_explanation` in the `annotations.json` file. To interpret them, you should also look at `referenced_models` which translates the temporary model name (in the prompt) to the actual output. Below, we provide more explanation as to what is happening behind the scenes.

ou can check the `raw_annotations["concise_explanation]` column in `annotations.json` (e.g. [here](https://github.com/tatsu-lab/alpaca_eval/tree/main/results/gpt4/alpaca_eval_cot_gpt4_turbo_fn/annotations.json)) which contains the chain of thought reasoning of the auto annotator. Note that the raw_annotations is not modified by the randomization of the order of the outputs. In particular, `"m"` and `"M"` can sometime refer to the first model (the reference) and sometime to the second model (the model being evaluated). To understand which model is being referred to, you should use the column `preference` and `ordered_models`. To make it easier we add a column `"referenced_models"` mapping the model names to the corresponding outputs. For example in the following annotation we see that the preference is 1.0 (i.e. `output_1`) and corresponds to model `M` in `concise_explanation` (see `ordered_models`).
You can check the `raw_annotations["concise_explanation]` column in `annotations.json` (e.g. [here](https://github.com/tatsu-lab/alpaca_eval/tree/main/results/gpt4/alpaca_eval_cot_gpt4_turbo_fn/annotations.json)) which contains the chain of thought reasoning of the auto annotator. Note that the raw_annotations is not modified by the randomization of the order of the outputs. In particular, `"m"` and `"M"` can sometime refer to the first model (the reference) and sometime to the second model (the model being evaluated). To understand which model is being referred to, you should use the column `preference` and `ordered_models`. To make it easier we add a column `"referenced_models"` mapping the model names to the corresponding outputs. For example in the following annotation we see that the preference is 1.0 (i.e. `output_1`) and corresponds to model `M` in `concise_explanation` (see `ordered_models`).

```json
{
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