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Achieving Tool Calling Functionality in LLMs Using Only Prompt Engineering Without Fine-Tuning

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arxiv 2407.04997 v1 pith:X5LH4P7Q submitted 2024-07-06 cs.SE cs.AIcs.HC

classification cs.SEcs.AIcs.HC
keywords callingllmstoolachievingcapabilitiesengineeringfine-tuningfunctionality
verification ladder T0 review T1 audit T2 compute T3 formal
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Currently, the vast majority of locally deployed open-source large language models (LLMs) and some commercial model interfaces do not support stable tool calling functionality. The existing solution involves fine-tuning LLMs, which results in significant time and computational resource consumption. This paper proposes a method that enables LLMs to achieve stable tool calling capabilities using only prompt engineering and some ingenious code design. We conducted experiments on multiple LLMs that lack tool calling capabilities across various tool calling tasks, achieving a success rate of 100%.

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Cited by 1 Pith paper

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  1. MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Steps

    cs.CL 2025-07 conditional novelty 4.0 of 10

    MRT, an LLM code-generation pipeline for Spanish table QA, achieves 85% accuracy on the IberLEF 2025 PRESTA test set.

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