mlsanityData to 7 Oct 2026

Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning

arXiv 2310.04474 ↗First posted 6 Oct 2023cs.SE cs.AI cs.PLcited by 16

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

Abstract (arXiv)

While enabling large language models to implement function calling (known as APIs) can greatly enhance the performance of Large Language Models (LLMs), function calling is still a challenging task due to the complicated relations between different APIs, especially in a context-learning setting without fine-tuning. This paper introduces ``Reverse Chain'', a controllable, target-driven approach designed to empower LLMs with the capability to operate external APIs only via prompts. Recognizing that most LLMs have limited tool-use capabilities, Reverse Chain limits LLMs to executing simple tasks, e.g., API Selection and Argument Completion. Furthermore, to manage a controllable multi-function calling, Reverse Chain adopts a generic rule based on a backward reasoning process. This rule determines when to do API selection or Argument completion. To evaluate the multi-tool-use capability of LLMs, we have released a compositional multi-tool task dataset, available at urlhttps://anonymous.4open.science/r/reverse-chain-8681. Extensive numerical experiments validate the remarkable proficiency of Reverse Chain in managing multiple API calls.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 14 of its 22 references that are in the map. First posted on arXiv 6 Oct 2023.

Builds on

14 of 22 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs31 Jul
  2. 2023ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases8 Jun
  3. 2023GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction30 May
  4. 2023Gorilla: Large Language Model Connected with Massive APIs24 May
  5. 2023Toolformer: Language Models Can Teach Themselves to Use Tools9 Feb

Other references · 9

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  4. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  5. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  6. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  7. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  8. 2023Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Modelscited by 659
  9. 2023TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIscited by 184

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 6; 2026: 6.

16 papers.

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By area:

  1. 2024Tool Learning with Large Language Models: A Surveycited by 221
  2. 2025A Survey of Context Engineering for Large Language Modelscited by 120
  3. 2025Retrieval Models Aren't Tool-Savvy: Benchmarking Tool Retrieval for Large Language Modelscited by 46
  4. 2024NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Callscited by 45
  5. 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 13
  6. 2025Enhancing Tool Learning in Large Language Models with Hierarchical Error Checklistscited by 7
  7. 2025CallNavi, A Challenge and Empirical Study on LLM Function Calling and Routingcited by 6
  8. 2024Deploying Foundation Model Powered Agent Services: A Surveycited by 4
  9. 2025ASTRA: A Negotiation Agent with Adaptive and Strategic Reasoning via Tool-integrated Action for Dynamic Offer Optimizationcited by 4
  10. 2026AsyncTool: Evaluating the Asynchronous Function Calling Capability under Multi-Task Scenarioscited by 3
  11. 2024SwissNYF: Tool Grounded LLM Agents for Black Box Settingcited by 0
  12. 2026Forward versus Backward: Comparing Reasoning Objectives in Direct Preference Optimizationcited by 0
4 more
  1. 2026A Policy Algebra for Trust-Preserving Agentic AI Executioncited by 0
  2. 2026Intent Engine: Natural-Language Intent Translation for Intent-Driven Orchestration in the Compute Continuumcited by 0
  3. 2026Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitivescited by 0
  4. 2026Construting Reverse Thinking: Developing Large Language Models' Reverse Thingking Abilitycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
  2. 2024Harnessing LLMs for API Interactions: A Framework for Classification and Synthetic Data Generation
  3. 2023CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
  4. 2024Designing Algorithms Empowered by Language Models: An Analytical Framework, Case Studies, and Insights
  5. 2026When the API Speaks the Wrong Language: Revisiting Post-Training for Multilingual Tool Use
  6. 2025Evaluating LLMs on Sequential API Call Through Automated Test Generation
  7. 2024Facilitating Multi-turn Function Calling for LLMs via Compositional Instruction Tuning
  8. 2024Octopus: On-device language model for function calling of software APIs