# Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation

arXiv: https://arxiv.org/abs/2412.01130 · HTML: https://mlsanity.com/paper/2412.01130/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use.
- Builds on 21 of its 30 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Placed in

- LLM agents and tool use / [Tool learning and function calling](https://mlsanity.com/agents/tool-learning-and-function-calling/)

## Builds on (most cited first, 21 of 21)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://mlsanity.com/paper/2301.13688/) (2023)
- [ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases](https://mlsanity.com/paper/2306.05301/) (2023)
- [A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis](https://mlsanity.com/paper/2307.12856/) (2023)
- [GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction](https://mlsanity.com/paper/2305.18752/) (2023)
- [Internet-Augmented Dialogue Generation](https://mlsanity.com/paper/2107.07566/) (2021)
- [ToolACE: Winning the Points of LLM Function Calling](https://mlsanity.com/paper/2409.00920/) (2024)
- [APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets](https://mlsanity.com/paper/2406.18518/) (2024)
- [ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models](https://mlsanity.com/paper/2305.18323/) (2023)
- [Solving Math Word Problems by Combining Language Models With Symbolic Solvers](https://mlsanity.com/paper/2304.09102/) (2023)
- [Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios](https://mlsanity.com/paper/2401.17167/) (2024)
- [Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks](https://mlsanity.com/paper/2407.00121/) (2024)
- [LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation](https://mlsanity.com/paper/2401.12224/) (2023)
- [Formally Specifying the High-Level Behavior of LLM-Based Agents](https://mlsanity.com/paper/2310.08535/) (2023)
- [Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Finance](https://mlsanity.com/paper/2401.15328/) (2024)

## Built on by (most cited first, 14 of 14)

- [The Landscape of Agentic Reinforcement Learning for LLMs: A Survey](https://mlsanity.com/paper/2509.02547/) (2025)
- [RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.03275/) (2025)
- [SkillFlow: Scalable and Efficient Agent Skill Retrieval System](https://mlsanity.com/paper/2504.06188/) (2025)
- [Krikri: Advancing Open Large Language Models for Greek](https://mlsanity.com/paper/2505.13772/) (2025)
- [Red-Teaming Coding Agents from a Tool-Invocation Perspective: An Empirical Security Assessment](https://mlsanity.com/paper/2509.05755/) (2025)
- [The Breeze 2 Herd of Models: Traditional Chinese LLMs Based on Llama with Vision-Aware and Function-Calling Capabilities](https://mlsanity.com/paper/2501.13921/) (2025)
- [Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use](https://mlsanity.com/paper/2602.20426/) (2026)
- [Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making](https://mlsanity.com/paper/2510.06222/) (2025)
- [Natural Language Tools: A Natural Language Approach to Tool Calling In Large Language Agents](https://mlsanity.com/paper/2510.14453/) (2025)
- [Agent Benchmarks Fail Public Sector Requirements](https://mlsanity.com/paper/2601.20617/) (2026)
- [Assertion-Conditioned Compliance: A Provenance-Aware Vulnerability in Multi-Turn Tool-Calling Agents](https://mlsanity.com/paper/2512.00332/) (2025)
- [ToolWeave: Structured Synthesis of Complex Multi-Turn Tool-Calling Dialogues](https://mlsanity.com/paper/2605.12521/) (2026)
- [From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix](https://mlsanity.com/paper/2609.01572/) (2026)
- [Arabic Prompts with English Tools: A Benchmark](https://mlsanity.com/paper/2601.05101/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have significantly advanced autonomous agents, particularly in zero-shot tool usage, also known as function calling. This research delves into enhancing the function-calling capabilities of LLMs by exploring different approaches, including prompt formats for integrating function descriptions, blending function-calling and instruction-following data, introducing a novel Decision Token for conditional prompts, leveraging chain-of-thought reasoning, and overcoming multilingual challenges with a translation pipeline. Our key findings and contributions are as follows: (1) Instruction-following data improves both function-calling accuracy and relevance detection. (2) The use of the newly proposed Decision Token, combined with synthetic non-function-call data, enhances relevance detection. (3) A tailored translation pipeline effectively overcomes multilingual limitations, demonstrating significant improvements in Traditional Chinese. These insights highlight the potential for improved function-calling capabilities and multilingual applications in LLMs.