From Symbolic Tasks to Code Generation: Diversification Yields Better Task Performers

• URL: http://arxiv.org/abs/2405.19787v2
• Date: Fri, 31 May 2024 01:23:41 GMT
• Title: From Symbolic Tasks to Code Generation: Diversification Yields Better Task Performers
• Authors: Dylan Zhang, Justin Wang, Francois Charton,
• Abstract summary: We show that a more diverse instruction set, extending beyond code-related tasks, improves the performance of code generation. Our observations suggest that a more diverse semantic space for instruction-tuning sets greatly improves the model's ability to follow instructions and perform tasks.
• Score: 1.6958018695660049
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Instruction tuning -- tuning large language models on instruction-output pairs -- is a promising technique for making models better adapted to the real world. Yet, the key factors driving the model's capability to understand and follow instructions not seen during training remain under-explored. Our investigation begins with a series of synthetic experiments within the theoretical framework of a Turing-complete algorithm called Markov algorithm, which allows fine-grained control over the instruction-tuning data. Generalization and robustness with respect to the training distribution emerge once a diverse enough set of tasks is provided, even though very few examples are provided for each task. We extend these initial results to a real-world application scenario of code generation and find that a more diverse instruction set, extending beyond code-related tasks, improves the performance of code generation. Our observations suggest that a more diverse semantic space for instruction-tuning sets greatly improves the model's ability to follow instructions and perform tasks.

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