Alopex: A Computational Framework for Enabling On-Device Function Calls with LLMs
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916473232949248 |
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| author | Ran, Yide Xu, Zhaozhuo Yao, Yuhang Hu, Zijian Han, Shanshan Jin, Han Shah, Alay Dilipbhai Zhang, Jipeng Stripelis, Dimitris Zhang, Tong Avestimehr, Salman He, Chaoyang |
| author_facet | Ran, Yide Xu, Zhaozhuo Yao, Yuhang Hu, Zijian Han, Shanshan Jin, Han Shah, Alay Dilipbhai Zhang, Jipeng Stripelis, Dimitris Zhang, Tong Avestimehr, Salman He, Chaoyang |
| contents | The rapid advancement of Large Language Models (LLMs) has led to their increased integration into mobile devices for personalized assistance, which enables LLMs to call external API functions to enhance their performance. However, challenges such as data scarcity, ineffective question formatting, and catastrophic forgetting hinder the development of on-device LLM agents. To tackle these issues, we propose Alopex, a framework that enables precise on-device function calls using the Fox LLM. Alopex introduces a logic-based method for generating high-quality training data and a novel ``description-question-output'' format for fine-tuning, reducing risks of function information leakage. Additionally, a data mixing strategy is used to mitigate catastrophic forgetting, combining function call data with textbook datasets to enhance performance in various tasks. Experimental results show that Alopex improves function call accuracy and significantly reduces catastrophic forgetting, providing a robust solution for integrating function call capabilities into LLMs without manual intervention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05209 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Alopex: A Computational Framework for Enabling On-Device Function Calls with LLMs Ran, Yide Xu, Zhaozhuo Yao, Yuhang Hu, Zijian Han, Shanshan Jin, Han Shah, Alay Dilipbhai Zhang, Jipeng Stripelis, Dimitris Zhang, Tong Avestimehr, Salman He, Chaoyang Artificial Intelligence Computation and Language The rapid advancement of Large Language Models (LLMs) has led to their increased integration into mobile devices for personalized assistance, which enables LLMs to call external API functions to enhance their performance. However, challenges such as data scarcity, ineffective question formatting, and catastrophic forgetting hinder the development of on-device LLM agents. To tackle these issues, we propose Alopex, a framework that enables precise on-device function calls using the Fox LLM. Alopex introduces a logic-based method for generating high-quality training data and a novel ``description-question-output'' format for fine-tuning, reducing risks of function information leakage. Additionally, a data mixing strategy is used to mitigate catastrophic forgetting, combining function call data with textbook datasets to enhance performance in various tasks. Experimental results show that Alopex improves function call accuracy and significantly reduces catastrophic forgetting, providing a robust solution for integrating function call capabilities into LLMs without manual intervention. |
| title | Alopex: A Computational Framework for Enabling On-Device Function Calls with LLMs |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2411.05209 |