Function-calling gets supercharged with a clever Decision Token mechanism
This paper enhances LLM function-calling capabilities through innovative prompt formats, data integration strategies, and a novel Decision Token mechanism. It also addresses multilingual limitations through a specialized translation pipeline, improving both accuracy and relevance detection.
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https://arxiv.org/abs/2412.01130
🔍 Original Problem:
Function-calling in LLMs faces challenges in prompt format variations, data integration, and multilingual support, limiting their effectiveness in real-world applications.
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🛠️ Solution in this Paper:
→ The paper introduces two distinct strategies for incorporating function descriptions into prompts: a dedicated role approach and system role integration.
→ A Decision Token mechanism improves relevance detection by forcing explicit classification before generating responses.
→ The solution combines instruction-following data with function-calling data to enhance overall performance.
→ A specialized translation pipeline addresses multilingual challenges, particularly for Traditional Chinese.
→ Chain-of-Thought reasoning is incorporated through synthetic data generation for improved function comprehension.
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💡 Key Insights:
→ Instruction-following data significantly improves function-calling accuracy
→ The Decision Token enhances relevance detection without compromising accuracy
→ Function descriptions in dedicated roles outperform system role integration
→ Multilingual capabilities can be effectively enhanced through targeted translation
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📊 Results:
→ Decision Token improved relevance detection by 65.42%
→ Achieved 84.63% AST Summary accuracy with system role integration
→ Translation pipeline showed significant improvements in Traditional Chinese function-calling
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