GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
GPT4Tools enables open-source LLMs to use multimodal tools via self-instruction, significantly improving tool invocation accuracy.
Key Findings
Methodology
GPT4Tools generates an instruction-following dataset using self-instruction, optimized with LoRA to enable open-source models like LLaMA and OPT to use tools. It prompts an advanced teacher model in multimodal contexts to create the dataset and employs LoRA for fine-tuning.
Key Results
- Vicuna-13B model shows a 9.3% improvement in tool invocation accuracy over GPT-3.5, performing well on unseen tools.
- LLaMA-13B's tool invocation success rate increased from 3.2% to 66.4% after fine-tuning.
- Experiments demonstrate GPT4Tools significantly enhances model performance in various visual tasks.
Significance
This research provides an efficient method for open-source LLMs to use multimodal tools, addressing issues of high computational cost and data inaccessibility, with significant impact on academia and industry.
Technical Contribution
GPT4Tools breaks the reliance on proprietary models by using self-instruction and LoRA optimization, offering a new benchmark for tool usage evaluation and significantly enhancing model tool invocation capabilities.
Novelty
First to apply self-instruction for tool usage in open-source models, differing from previous work relying on proprietary models, introducing a new multimodal context self-instruction method.
Limitations
- Model performance on complex multimodal tasks still needs improvement, possibly requiring more sophisticated instruction data.
- While improved, tool invocation on unseen tools is still not on par with proprietary models.
Future Work
Future work could explore more complex multimodal tasks, optimize instruction generation strategies, and extend to more types of tool usage.
AI Executive Summary
GPT4Tools enables open-source large language models to use multimodal tools through self-instruction, addressing the high computational cost and data inaccessibility of existing models. By generating instruction datasets and combining with LoRA optimization, GPT4Tools enhances model performance in tasks like visual comprehension and image generation.
Experimental results show that the fine-tuned Vicuna-13B model improves tool invocation accuracy by 9.3% over GPT-3.5 and performs well on unseen tools. This indicates that GPT4Tools not only enhances model tool invocation capabilities but also strengthens its zero-shot ability on unseen tools.
This research provides an efficient method for open-source LLMs to use multimodal tools, with significant impact on academia and industry. Future research can explore more complex multimodal tasks, optimize instruction generation strategies, and extend to more types of tool usage.
Deep Analysis
Background
Recent advances in large language models like GPT-3 and ChatGPT have shown significant potential in zero-shot learning and logical reasoning. However, these models typically rely on high computational costs and inaccessible data, limiting their application in multimodal tool usage.
Core Problem
Existing large language models face challenges in using multimodal tools due to high computational costs and data inaccessibility, limiting their widespread application in practical scenarios.
Innovation
GPT4Tools uses self-instruction to generate datasets, enabling open-source models to use multimodal tools. Unlike previous methods relying on proprietary models, GPT4Tools achieves tool usage capability through multimodal context self-instruction.
Methodology
- �� Generate instruction datasets: Use advanced teacher models in multimodal contexts to create instruction datasets.
- �� LoRA optimization: Employ LoRA to fine-tune open-source models for tool usage.
- �� Evaluation benchmark: Provide a new benchmark for tool usage evaluation.
Experiments
Experiments used models like Vicuna-13B and LLaMA-13B to evaluate tool invocation performance. Comparisons with GPT-3.5 validated the effectiveness of GPT4Tools.
Results
Results show that the fine-tuned Vicuna-13B model improves tool invocation accuracy by 9.3% over GPT-3.5 and performs well on unseen tools.
Applications
GPT4Tools can be applied to multimodal tasks like visual comprehension and image generation, suitable for scenarios requiring efficient tool invocation.
Limitations & Outlook
Model performance on complex multimodal tasks still needs improvement. Future work could explore more complex tasks and optimize instruction generation strategies.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. GPT4Tools is like a cookbook that teaches you how to use different kitchen tools to complete various dishes. By reading the cookbook, you not only learn to use common tools but can quickly adapt to new ones you've never seen. This process is similar to how GPT4Tools generates instruction datasets to teach models to use multimodal tools.
ELI14 Explained like you're 14
Imagine you're playing a game with lots of tools to complete tasks. GPT4Tools is like the game's tutorial, teaching you how to use these tools. Even if it's a tool you've never seen, the tutorial helps you master it quickly. In this way, GPT4Tools helps models learn to use multimodal tools, just like you learn to use new tools in a game.
Glossary
Self-instruction
A method for generating instruction datasets by prompting advanced models in multimodal contexts.
Used to generate tool usage instruction datasets.
LoRA Optimization
An optimization technique that freezes model parameters and only optimizes low-rank decomposition components.
Used for fine-tuning open-source large language models.
Multimodal Tools
Tools capable of handling data from multiple modalities, such as vision and text.
Models use these tools to solve visual problems.
Zero-shot Capability
The ability to perform well on data or tasks not seen during training.
Model performs well on unseen tools.
Tool Invocation Accuracy
The frequency with which a model correctly invokes tools.
A metric for evaluating model tool usage capabilities.
Open Questions Unanswered questions from this research
- 1 How to improve model performance on more complex multimodal tasks?
- 2 How to optimize instruction generation strategies to support more types of tools?
Applications
Immediate Applications
Visual Comprehension
GPT4Tools can be used for tasks like image recognition and visual question answering, helping models quickly understand visual content.
Long-term Vision
Multimodal AI Systems
By extending GPT4Tools' capabilities, future development can build more powerful multimodal AI systems for more complex tasks.
Abstract
This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have shown great potential for tool usage through sophisticated prompt engineering. Nevertheless, these models typically rely on prohibitive computational costs and publicly inaccessible data. To address these challenges, we propose the GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and OPT, to use tools. It generates an instruction-following dataset by prompting an advanced teacher with various multi-modal contexts. By using the Low-Rank Adaptation (LoRA) optimization, our approach facilitates the open-source LLMs to solve a range of visual problems, including visual comprehension and image generation. Moreover, we provide a benchmark to evaluate the ability of LLMs to use tools, which is performed in both zero-shot and fine-tuning ways. Extensive experiments demonstrate the effectiveness of our method on various language models, which not only significantly improves the accuracy of invoking seen tools, but also enables the zero-shot capacity for unseen tools. The code and demo are available at https://github.com/StevenGrove/GPT4Tools.