This Eval helps evaluate the summarization results of a summarization task. The template variables are:
document: The document text to summarize
summary: The summary of the document
Summarization Eval Template
You are comparing the summary text and it's original document and trying to determine
if the summary is good. Here is the data:
[BEGIN DATA]
************
[Summary]: {summary}
************
[Original Document]: {document}
[END DATA]
Compare the Summary above to the Original Document and determine if the Summary is
comprehensive, concise, coherent, and independent relative to the Original Document.
Your response must be a string, either good or bad, and should not contain any text
or characters aside from that. The string bad means that the Summary is not comprehensive, concise,
coherent, and independent relative to the Original Document. The string good means the Summary
is comprehensive, concise, coherent, and independent relative to the Original Document.
We are continually iterating our templates, view the most up-to-date template on GitHub. Last updated on 10/12/2023
Benchmark Results
GPT-4 Results
GPT-3.5 Results
Claud V2 Results
GPT-4 Turbo
How To Run the Eval
import phoenix.experimental.evals.templates.default_templates as templatesfrom phoenix.experimental.evals import ( OpenAIModel, download_benchmark_dataset, llm_classify,)model =OpenAIModel( model_name="gpt-4", temperature=0.0,)#The rails is used to hold the output to specific values based on the template#It will remove text such as ",,," or "..."#Will ensure the binary value expected from the template is returned rails =list(templates.SUMMARIZATION_PROMPT_RAILS_MAP.values())summarization_classifications =llm_classify( dataframe=df_sample, template=templates.SUMMARIZATION_PROMPT_TEMPLATE, model=model, rails=rails, provide_explanation=True, #optional to generate explanations for the value produced by the eval LLM)
The above shows how to use the summarization Eval template.
