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Summarization

Eval Recipe for model migration

This Eval Recipe demonstrates how to compare performance of a Summarization prompt with Gemini 1.0 and Gemini 2.0 using a labeled dataset and open source evaluation tool Promptfoo.

  • Use case: summarize a news article.

  • The Evaluation Dataset1 includes 5 news articles stored as plain text files, and a JSONL file with ground truth labels: dataset.jsonl. Each record in this file includes 2 attributes wrapped in the vars object. This structure allows Promptfoo to inject the article text into the prompt template, and find ground truth label required to score the quality of model-generated summaries:

    • document: relative path to the plain text file containing the news article
    • summary: ground truth label (short summary of the article)

  • Prompt Template is a zero-shot prompt located in prompt_template.txt with variable document that gets populated from the corresponding dataset attribute.

  • promptfooconfig.yaml contains all Promptfoo configuration:

    • providers: list of models that will be evaluated
    • prompts: location of the prompt template file
    • tests: location of the labeled dataset file
    • defaultTest: defines the scoring logic:
      1. type: rouge-n rates similarity between the model response and the ground truth label
      2. value: "{{summary}}" instructs Promptfoo to use the "summary" dataset attribute as the ground truth label

How to run this Eval Recipe

  • Google Cloud Shell is the easiest option as it automatically clones our Github repo:

    Open in Cloud Shell

  • Alternatively, you can use the following command to clone this repo to any Linux environment with configured Google Cloud Environment:

    git clone --filter=blob:none --sparse https://github.com/GoogleCloudPlatform/applied-ai-engineering-samples.git && \
cd applied-ai-engineering-samples && \
git sparse-checkout init && \
git sparse-checkout set genai-on-vertex-ai/gemini/model_upgrades && \
git pull origin main
cd genai-on-vertex-ai/gemini/model_upgrades

  • Install Promptfoo using these instructions.

  • Navigate to the Eval Recipe directory in terminal and run the command promptfoo eval.

    cd summarization/promptfoo
promptfoo eval

  • Run promptfoo view to analyze the eval results. You can switch the Display option to Show failures only in order to investigate any underperforming prompts.

How to customize this Eval Recipe:

  1. Copy the configuration file promptfooconfig.yaml to a new folder.
  2. Add your labeled dataset file with JSONL schema similar to dataset.jsonl.
  3. Save your prompt template to prompt_template.txt and make sure that the template variables map to the variables defined in your dataset.
  4. That's it! You are ready to run promptfoo eval. If needed, add alternative prompt templates or additional metrics to promptfooconfig.yaml as explained here.

  1. Dataset (XSum) citation: @InProceedings{xsum-emnlp, author = {Shashi Narayan and Shay B. Cohen and Mirella Lapata}, title = {Don't Give Me the Details, Just the Summary! {T}opic-Aware Convolutional Neural Networks for Extreme Summarization}, booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}, year = {2018}, address = {Brussels, Belgium}, } ↩