Knowledge Extraction from Rationalization
Description:
Rationalization is a type of example-specific explanation.
However, samples belonging to the same class might share similar rationales.
The idea is to define ways to go from a local explanation (i.e., rationalization) to a global explanation (i.e., knowledge base) by aggregating and summarizing extracted rationales.
This can be done with LLMs (e.g., prompting techniques) or other solutions.
Contact: Federico Ruggeri
References:
A Game Theoretic Approach to Class-wise Selective Rationalization
Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola.
33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2019.
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