Functionality learning through specification instructions

• URL: http://arxiv.org/abs/2311.08481v2
• Date: Wed, 09 Oct 2024 11:54:38 GMT
• Title: Functionality learning through specification instructions
• Authors: Pedro Henrique Luz de Araujo, Benjamin Roth,
• Abstract summary: Test suites assess natural language processing models' performance on specific functionalities. This paper introduces specification instructions: text descriptions specifying fine-grained task-specific behaviors. We combine the specification instructions to create specification-augmented prompts, which we feed to language models pre-trained on natural instruction data.
• Score: 2.4095382017500464
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Test suites assess natural language processing models' performance on specific functionalities: cases of interest involving model robustness, fairness, or particular linguistic capabilities. This paper introduces specification instructions: text descriptions specifying fine-grained task-specific behaviors. For each functionality in a suite, we generate an instruction that describes it. We combine the specification instructions to create specification-augmented prompts, which we feed to language models pre-trained on natural instruction data. We conduct experiments to measure how optimizing for some functionalities may negatively impact functionalities that are not covered by the specification set. Our analyses across four tasks and models of diverse sizes and families show that smaller models struggle to follow specification instructions. However, larger models (>~3B params.) can benefit from specifications and -- surprisingly -- even generalize certain desirable behaviors across functionalities.

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