SASEvaluator returns score even when predicted answer is None

[mrm1001]

In a RAG pipeline, there are cases where the prompt instructs the LLM to return a placeholder answer if there is missing information in the context, such as "No answer". See an example in our evaluation repository.

In that case, it makes sense to replace the predicted answer in the RAG pipeline with None, instead of computing any metric on the placeholder answer. However, the SASEvaluator returns a score even if None is passed.

Example:

from haystack.components.evaluators import SASEvaluator

sas = SASEvaluator(model="sentence-transformers/all-mpnet-base-v2")
sas.warm_up()
sas.run(predicted_answers=[None, "Whatever"], ground_truth_answers=["Whatever", "Another whatever"])

the output is:

{'score': 0.46286168694496155,
 'individual_scores': [0.26153379678726196, 0.6641895771026611]}

Expected

{'score': 0.6641895771026611,
 'individual_scores': [None, 0.6641895771026611]}

[lbux]

In that case, it makes sense to replace the predicted answer in the RAG pipeline with None

This is something that I'm manually doing in my workflow, but I would like to see a component for. We have DocumentCleaner, so I think it would make sense to have an AnswerCleaner or a GeneratedAnswerCleaner which can take in a "stop word" parameter to scan the answer for a specific word/phrase.

The handling of 'None' might need to be applied to LLMEvaluator as well. I run a forked version and it accepts 'None' as a parameter and returns an answer. I will try running it on the main Haystack code to see what happens.

[lbux]

The same issue needs to be handled in LLMEvaluators.