From Symbolic Tasks to Code Generation: Diversification Yields Better Task Performers
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916266758897664 |
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| author | Zhang, Dylan Wang, Justin Charton, Francois |
| author_facet | Zhang, Dylan Wang, Justin Charton, Francois |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19787 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Symbolic Tasks to Code Generation: Diversification Yields Better Task Performers Zhang, Dylan Wang, Justin Charton, Francois Computation and Language Artificial Intelligence Machine Learning Logic in Computer Science Programming Languages 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. |
| title | From Symbolic Tasks to Code Generation: Diversification Yields Better Task Performers |
| topic | Computation and Language Artificial Intelligence Machine Learning Logic in Computer Science Programming Languages |
| url | https://arxiv.org/abs/2405.19787 |