itayle/diverse-demonstrations
Diverse Demonstrations Improve In-context Compositional Generalization
This project helps researchers working with large language models improve how these models generalize to new, complex instructions. By carefully selecting a diverse set of examples to show the model, rather than just similar ones, it helps the model learn to construct new logical structures. This is particularly useful for AI researchers and machine learning engineers developing more robust semantic parsing or natural language understanding systems.
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Use this if you are an AI researcher or machine learning engineer struggling with large language models failing to generalize to new, unseen compositional structures in tasks like semantic parsing.
Not ideal if you are a practitioner looking for a ready-to-use, off-the-shelf solution for general natural language processing tasks without deep involvement in model training.
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Python
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Last pushed
Jul 07, 2023
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