mlsanityData to 14 Oct 2026

Planning and Editing What You Retrieve for Enhanced Tool Learning

arXiv 2404.00450 ↗First posted 30 Mar 2024cs.CL cs.AI cs.IRcited by 21

On the map: LLM reasoning → Reasoning for agents, planning and code

also in 1 more
Abstract (arXiv)

Recent advancements in integrating external tools with Large Language Models (LLMs) have opened new frontiers, with applications in mathematical reasoning, code generators, and smart assistants. However, existing methods, relying on simple one-time retrieval strategies, fall short on effectively and accurately shortlisting relevant tools. This paper introduces a novel PLUTO (Planning, Learning, and Understanding for TOols) approach, encompassing `Plan-and-Retrieve (P&R)` and `Edit-and-Ground (E&G)` paradigms. The P&R paradigm consists of a neural retrieval module for shortlisting relevant tools and an LLM-based query planner that decomposes complex queries into actionable tasks, enhancing the effectiveness of tool utilization. The E&G paradigm utilizes LLMs to enrich tool descriptions based on user scenarios, bridging the gap between user queries and tool functionalities. Experiment results demonstrate that these paradigms significantly improve the recall and NDCG in tool retrieval tasks, significantly surpassing current state-of-the-art models.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 25 of its 36 references that are in the map. First posted on arXiv 30 Mar 2024.

Builds on

25 of 36 references.

References · 25

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  6. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  7. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  8. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
  9. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  10. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
  11. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  12. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 832
13 more
  1. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  2. 2019Latent Retrieval for Weakly Supervised Open Domain Question Answeringcited by 695
  3. 2022Visual Programming: Compositional visual reasoning without trainingcited by 516
  4. 2023ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Casescited by 393
  5. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  6. 2022Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoningcited by 350
  7. 2023MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoningcited by 316
  8. 2023ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddingscited by 240
  9. 2023GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Informationcited by 117
  10. 2023Large Language Models for Automated Open-domain Scientific Hypotheses Discoverycited by 100
  11. 2023Exploring Continual Learning for Code Generation Modelscited by 22
  12. 2023Don't Fine-Tune, Decode: Syntax Error-Free Tool Use via Constrained Decodingcited by 13
  13. 2022Dialog2API: Task-Oriented Dialogue with API Description and Example Programscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 8; 2026: 8.

21 papers.

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  1. 2024Tool Learning with Large Language Models: A Surveycited by 223
  2. 2024Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Basescited by 23
  3. 2024Guided Profile Generation Improves Personalization with LLMscited by 19
  4. 2024Enhancing Tool Retrieval with Iterative Feedback from Large Language Modelscited by 17
  5. 2025Tool-to-Agent Retrieval: Bridging Tools and Agents for Scalable LLM Multi-Agent Systemscited by 15
  6. 2025Teaching Language Models To Gather Information Proactivelycited by 14
  7. 2025GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulationcited by 10
  8. 2025Comparison of Text-Based and Image-Based Retrieval in Multimodal Retrieval Augmented Generation Large Language Model Systemscited by 5
  9. 2024FoodPuzzle: Developing Large Language Model Agents as Flavor Scientistscited by 4
  10. 2025GraphicBench: A Planning Benchmark for Graphic Design with Language Agentscited by 4
  11. 2025VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interactioncited by 2
  12. 2025MENTOR: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillationcited by 2
9 more
  1. 2026Multi-Field Tool Retrievalcited by 2
  2. 2026Tool Retrieval Bridge: Aligning Vague Instructions with Retriever Preferences via Bridge Modelcited by 2
  3. 2026Enhancing Tool Calling in LLMs with the International Tool Calling Datasetcited by 1
  4. 2026MagicSelector: Joint Optimization for Agent Tool Selection via Counterfactual Decomposition and Progressive Rerankingcited by 1
  5. 2025TUMS: Enhancing Tool-use Abilities of LLMs with Multi-structure Handlerscited by 0
  6. 2026Enrich-Retrieve-Rank: Scaling Capability Discovery Beyond In-Context Routingcited by 0
  7. 2026Toollery: Scaling LLM Agents to Thousands of Skills and Toolscited by 0
  8. 2026Toward Interactive Understanding of Code APIscited by 0
  9. 2026Targeting Pivotal Decisions for Credit Assignment in Agentic Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025LoSemB: Logic-Guided Semantic Bridging for Inductive Tool Retrieval
  2. 2024Towards Completeness-Oriented Tool Retrieval for Large Language Models
  3. 2024Efficient and Scalable Estimation of Tool Representations in Vector Space
  4. 2024Data-Efficient Massive Tool Retrieval: A Reinforcement Learning Approach for Query-Tool Alignment with Language Models
  5. 2024LLM With Tools: A Survey
  6. 2024ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval
  7. 2026PORTS: Preference-Optimized Retrievers for Tool Selection with Large Language Models
  8. 2025Tools are under-documented: Simple Document Expansion Boosts Tool Retrieval