# TOOLVERIFIER: Generalization to New Tools via Self-Verification

arXiv: https://arxiv.org/abs/2402.14158 · HTML: https://mlsanity.com/paper/2402.14158/

## Key facts

- Cited by 24 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 33 of its 40 references that are in the map.
- First posted on arXiv 21 Feb 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, 30 of 33)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (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)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [Measuring and Narrowing the Compositionality Gap in Language Models](https://mlsanity.com/paper/2210.03350/) (2022)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [PAL: Program-aided Language Models](https://mlsanity.com/paper/2211.10435/) (2022)
- [Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor](https://mlsanity.com/paper/2212.09689/) (2022)
- [ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases](https://mlsanity.com/paper/2306.05301/) (2023)
- [Chain-of-Verification Reduces Hallucination in Large Language Models](https://mlsanity.com/paper/2309.11495/) (2023)
- [ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving](https://mlsanity.com/paper/2309.17452/) (2023)
- [GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction](https://mlsanity.com/paper/2305.18752/) (2023)
- [ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings](https://mlsanity.com/paper/2305.11554/) (2023)
- [TALM: Tool Augmented Language Models](https://mlsanity.com/paper/2205.12255/) (2022)
- [Generating Datasets with Pretrained Language Models](https://mlsanity.com/paper/2104.07540/) (2021)
- [RestGPT: Connecting Large Language Models with Real-World RESTful APIs](https://mlsanity.com/paper/2306.06624/) (2023)
- [On the Tool Manipulation Capability of Open-source Large Language Models](https://mlsanity.com/paper/2305.16504/) (2023)
- [Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models](https://mlsanity.com/paper/2308.00675/) (2023)
- [ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models](https://mlsanity.com/paper/2305.14323/) (2023)
- [Learning to Reason and Memorize with Self-Notes](https://mlsanity.com/paper/2305.00833/) (2023)
- [The ART of LLM Refinement: Ask, Refine, and Trust](https://mlsanity.com/paper/2311.07961/) (2023)
- [Teaching Language Models to Self-Improve through Interactive Demonstrations](https://mlsanity.com/paper/2310.13522/) (2023)
- [SELF: Self-Evolution with Language Feedback](https://mlsanity.com/paper/2310.00533/) (2023)

## Built on by (most cited first, 24 of 24)

- [Tool Learning with Large Language Models: A Survey](https://mlsanity.com/paper/2405.17935/) (2024)
- [Prompt Injection Attack to Tool Selection in LLM Agents](https://mlsanity.com/paper/2504.19793/) (2025)
- [Self-Challenging Language Model Agents](https://mlsanity.com/paper/2506.01716/) (2025)
- [CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks](https://mlsanity.com/paper/2507.23751/) (2025)
- [Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources](https://mlsanity.com/paper/2409.08239/) (2024)
- [TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use](https://mlsanity.com/paper/2412.15495/) (2024)
- [GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation](https://mlsanity.com/paper/2502.18990/) (2025)
- [ToolForge: A Data Synthesis Pipeline for Multi-Hop Search without Real-World APIs](https://mlsanity.com/paper/2512.16149/) (2025)
- [When is the consistent prediction likely to be a correct prediction?](https://mlsanity.com/paper/2407.05778/) (2024)
- [Tool Verification for Test-Time Reinforcement Learning](https://mlsanity.com/paper/2603.02203/) (2026)
- [LLMs can learn self-restraint through iterative self-reflection](https://mlsanity.com/paper/2405.13022/) (2024)
- [Controllable and Verifiable Tool-Use Data Synthesis for Agentic Reinforcement Learning](https://mlsanity.com/paper/2604.09813/) (2026)
- [Frontier AI Ethics: Anticipating and Evaluating the Societal Impacts of Language Model Agents](https://mlsanity.com/paper/2404.06750/) (2024)
- [GraphChain: Large Language Models for Large-scale Graph Analysis via Tool Chaining](https://mlsanity.com/paper/2511.00457/) (2025)
- [NaviAgent: Graph-Driven Bilevel Planning for Scalable Tool Orchestration](https://mlsanity.com/paper/2506.19500/) (2025)
- [Teaching LLMs to Learn Tool Trialing and Execution through Environment Interaction](https://mlsanity.com/paper/2601.12762/) (2026)
- [SEAL: Suite for Evaluating API-use of LLMs](https://mlsanity.com/paper/2409.15523/) (2024)
- [IEA: Amateur-Friendly Conversational Image Editing Agent via Three Stages of Multitask Alignment](https://mlsanity.com/paper/2606.08016/) (2026)
- [TUMS: Enhancing Tool-use Abilities of LLMs with Multi-structure Handlers](https://mlsanity.com/paper/2505.08402/) (2025)
- [TAPS: Tool-Augmented Personalisation via Structured Tagging](https://mlsanity.com/paper/2506.20409/) (2025)
- [Robust and Efficient Tool Orchestration via Layered Execution Structures with Reflective Correction](https://mlsanity.com/paper/2602.18968/) (2026)
- [SciToolAgent-Evo: An Ontology-Aware Self-Evolving Agent for Open-World Scientific Tool Acquisition](https://mlsanity.com/paper/2607.28692/) (2026)
- [Toolcompass: Guiding Tool Trialing, Not Suppressing It](https://mlsanity.com/paper/2609.25678/) (2026)
- [One Task, Many Interfaces: Simple Cross-Interface Consistency Training Improves LLM Agent Robustness](https://mlsanity.com/paper/2610.20576/) (2026)

## Abstract (arXiv)

Teaching language models to use tools is an important milestone towards building general assistants, but remains an open problem. While there has been significant progress on learning to use specific tools via fine-tuning, language models still struggle with learning how to robustly use new tools from only a few demonstrations. In this work we introduce a self-verification method which distinguishes between close candidates by self-asking contrastive questions during (1) tool selection; and (2) parameter generation. We construct synthetic, high-quality, self-generated data for this goal using Llama-2 70B, which we intend to release publicly. Extensive experiments on 4 tasks from the ToolBench benchmark, consisting of 17 unseen tools, demonstrate an average improvement of 22% over few-shot baselines, even in scenarios where the distinctions between candidate tools are finely nuanced.