Summary of Alopex: a Computational Framework For Enabling On-device Function Calls with Llms, by Yide Ran et al.
Alopex: A Computational Framework for Enabling On-Device Function Calls with LLMs
by Yide Ran, Zhaozhuo Xu, Yuhang Yao, Zijian Hu, Shanshan Han, Han Jin, Alay Dilipbhai Shah, Jipeng Zhang, Dimitris Stripelis, Tong Zhang, Salman Avestimehr, Chaoyang He
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework called Alopex is proposed to integrate Large Language Models (LLMs) with external API functions on mobile devices. The framework enables precise on-device function calls using the Fox LLM and addresses challenges such as data scarcity, ineffective question formatting, and catastrophic forgetting. 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. 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Alopex is a new way to connect Large Language Models (LLMs) with other tools on phones. This helps people get personalized help from their devices. But there are problems like not having enough data, asking the wrong questions, and forgetting what they learned. The Alopex system solves these issues by creating good training data and a special format for learning from mistakes. It also mixes different types of data to make sure the model remembers what it learned before. This makes the LLMs work better and helps people get the help they need without needing to do anything extra. |
Keywords
» Artificial intelligence » Fine tuning
