Re-Initialization Token Learning for Tool-Augmented Large Language Models

• URL: http://arxiv.org/abs/2506.14248v1
• Date: Tue, 17 Jun 2025 07:11:00 GMT
• Title: Re-Initialization Token Learning for Tool-Augmented Large Language Models
• Authors: Chenghao Li, Liu Liu, Baosheng Yu, Jiayan Qiu, Yibing Zhan,
• Abstract summary: Large language models have demonstrated exceptional performance, yet struggle with complex tasks such as numerical reasoning, plan generation.<n>We propose a novel token learning method that aligns tool tokens with the existing word embedding space.<n>We evaluate the method on tasks such as numerical reasoning, knowledge-based question answering, and embodied plan generation using GSM8K-XL, FuncQA, KAMEL, and VirtualHome datasets.
• Score: 49.91503552002649
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models have demonstrated exceptional performance, yet struggle with complex tasks such as numerical reasoning, plan generation. Integrating external tools, such as calculators and databases, into large language models (LLMs) is crucial for enhancing problem-solving capabilities. Current methods assign a unique token to each tool, enabling LLMs to call tools through token prediction-similar to word generation. However, this approach fails to account for the relationship between tool and word tokens, limiting adaptability within pre-trained LLMs. To address this issue, we propose a novel token learning method that aligns tool tokens with the existing word embedding space from the perspective of initialization, thereby enhancing model performance. We begin by constructing prior token embeddings for each tool based on the tool's name or description, which are used to initialize and regularize the learnable tool token embeddings. This ensures the learned embeddings are well-aligned with the word token space, improving tool call accuracy. We evaluate the method on tasks such as numerical reasoning, knowledge-based question answering, and embodied plan generation using GSM8K-XL, FuncQA, KAMEL, and VirtualHome datasets. The results demonstrate clear improvements over recent baselines, including CoT, REACT, ICL, and ToolkenGPT, indicating that our approach effectively augments LLMs with tools through relevant tokens across diverse domains.

Related papers

• ToolGen: Unified Tool Retrieval and Calling via Generation [34.34787641393914]
  We introduce ToolGen, a paradigm shift that integrates tool knowledge directly into the large language models' parameters.<n>We show that ToolGen achieves superior results in both tool retrieval and autonomous task completion.<n>ToolGen paves the way for more versatile, efficient, and autonomous AI systems.
  arXiv  Detail & Related papers  (2024-10-04T13:52:32Z)
• LLM With Tools: A Survey [0.0]
  This paper delves into the methodology,challenges, and developments in the realm of teaching LLMs to use external tools. We introduce a standardized paradigm for tool integration guided by a series of functions that map user instructions to actionable plans. Our exploration reveals the various challenges encountered, such as tool invocation timing, selection accuracy, and the need for robust reasoning processes.
  arXiv  Detail & Related papers  (2024-09-24T14:08:11Z)
• Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents [56.822238860147024]
  Augmenting large language models with external tools has emerged as a promising approach to extend their utility.<n>Previous methods manually parse tool documentation and create in-context demonstrations, transforming tools into structured formats for LLMs to use in their step-by-step reasoning.<n>We propose AutoTools, a framework that enables LLMs to automate the tool-use workflow.
  arXiv  Detail & Related papers  (2024-05-26T11:40:58Z)
• Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
  Real-world systems often incorporate a wide array of tools, making it impractical to input all tools into Large Language Models. Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions. We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
  arXiv  Detail & Related papers  (2024-05-25T06:41:23Z)
• Look Before You Leap: Towards Decision-Aware and Generalizable Tool-Usage for Large Language Models [26.28459880766842]
  We propose a decision-aware and generalizable tool-usage framework (DEER) Specifically, we first construct the tool-usage samples with multiple decision branches via an automatic generation pipeline. Our proposed DEER is effective and significantly outperforms baselines across various datasets.
  arXiv  Detail & Related papers  (2024-02-26T16:11:03Z)
• Efficient Tool Use with Chain-of-Abstraction Reasoning [63.08202389132155]
  Large language models (LLMs) need to ground their reasoning to real-world knowledge.<n>There remains challenges for fine-tuning LLM agents to invoke tools in multi-step reasoning problems.<n>We propose a new method for LLMs to better leverage tools in multi-step reasoning.
  arXiv  Detail & Related papers  (2024-01-30T21:53:30Z)
• CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets [75.64181719386497]
  We present CRAFT, a tool creation and retrieval framework for large language models (LLMs) It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks. Our method is designed to be flexible and offers a plug-and-play approach to adapt off-the-shelf LLMs to unseen domains and modalities, without any finetuning.
  arXiv  Detail & Related papers  (2023-09-29T17:40:26Z)
• CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models [74.22729793816451]
  Large Language Models (LLMs) have made significant progress in utilizing tools, but their ability is limited by API availability. We propose CREATOR, a novel framework that enables LLMs to create their own tools using documentation and code realization. We evaluate CREATOR on MATH and TabMWP benchmarks, respectively consisting of challenging math competition problems.
  arXiv  Detail & Related papers  (2023-05-23T17:51:52Z)
• ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings [25.5476046472217]
  Augmenting large language models with external tools has emerged as a promising approach to solving complex problems. Recent in-context learning paradigm alleviates these issues, but the limited context length only allows for a few shots of demonstrations. We propose an alternative approach, $textbfToolkenGPT$, which combines the benefits of both sides.
  arXiv  Detail & Related papers  (2023-05-19T09:54:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.