mlsanityData to 14 Oct 2026

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

arXiv 2412.01130 ↗First posted 2 Dec 2024cs.CLcited by 14

On the map: LLM agents and tool use → Tool learning and function calling

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.

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.

Builds on

21 of 30 references.

Closest ancestors · 8

Same direction, newest first

  1. 2024ToolACE: Winning the Points of LLM Function Calling2 Sep
  2. 2024Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks27 Jun
  3. 2024APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets26 Jun
  4. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs31 Jul
  5. 2023ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases8 Jun
  6. 2023GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction30 May
  7. 2023Gorilla: Large Language Model Connected with Massive APIs24 May
  8. 2023Toolformer: Language Models Can Teach Themselves to Use Tools9 Feb

Other references · 13

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  4. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  5. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 739
  6. 2023A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesiscited by 353
  7. 2021Internet-Augmented Dialogue Generationcited by 217
  8. 2023ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Modelscited by 184
  9. 2023Solving Math Word Problems by Combining Language Models With Symbolic Solverscited by 124
  10. 2024Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarioscited by 86
  11. 2023LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automationcited by 40
  12. 2023Formally Specifying the High-Level Behavior of LLM-Based Agentscited by 15
1 more
  1. 2024Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Financecited by 14

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 9; 2026: 5.

14 papers.

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By area:

  1. 2025The Landscape of Agentic Reinforcement Learning for LLMs: A Surveycited by 205
  2. 2025RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generationcited by 28
  3. 2025SkillFlow: Scalable and Efficient Agent Skill Retrieval Systemcited by 22
  4. 2025Krikri: Advancing Open Large Language Models for Greekcited by 14
  5. 2025Red-Teaming Coding Agents from a Tool-Invocation Perspective: An Empirical Security Assessmentcited by 5
  6. 2025The Breeze 2 Herd of Models: Traditional Chinese LLMs Based on Llama with Vision-Aware and Function-Calling Capabilitiescited by 4
  7. 2026Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Usecited by 4
  8. 2025Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Makingcited by 3
  9. 2025Natural Language Tools: A Natural Language Approach to Tool Calling In Large Language Agentscited by 3
  10. 2026Agent Benchmarks Fail Public Sector Requirementscited by 3
  11. 2025Assertion-Conditioned Compliance: A Provenance-Aware Vulnerability in Multi-Turn Tool-Calling Agentscited by 1
  12. 2026ToolWeave: Structured Synthesis of Complex Multi-Turn Tool-Calling Dialoguescited by 1
2 more
  1. 2026From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mixcited by 1
  2. 2026Arabic Prompts with English Tools: A Benchmarkcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Improving Small-Scale Large Language Models Function Calling for Reasoning Tasks
  2. 2025Cross-Lingual Prompt Steerability: Towards Accurate and Robust LLM Behavior across Languages
  3. 2025Small Models, Big Tasks: An Exploratory Empirical Study on Small Language Models for Function Calling
  4. 2023An Empirical Study of Instruction-tuning Large Language Models in Chinese
  5. 2026Lost in Execution: On the Multilingual Robustness of Tool Calling in Large Language Models
  6. 2025Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysis
  7. 2023CMMLU: Measuring massive multitask language understanding in Chinese
  8. 2025LongFuncEval: Measuring the effectiveness of long context models for function calling