Home > Article > Technology peripherals > Tsinghua University and other open source 'tool learning benchmark' ToolBench, fine-tuning model ToolLLaMA performance surpasses ChatGPT
Human beings have the ability to create and utilize tools, allowing us to break through the limitations of the body and explore a wider world.
The basic model of artificial intelligence is similar. If you only rely on the weights obtained in the training stage, the usage scenarios will be very limited. However, the recently proposed tool learning combines specialized tools in specific fields with large-scale The combination of basic models can achieve higher efficiency and performance.
However, the current research on tool learning is not in-depth enough, and there is a lack of relevant open source data and code.
Recently, OpenBMB (Open Lab for Big Model Base), an open source community supported by Tsinghua University Natural Language Processing Laboratory and others, released the ToolBench project, which can help developers build open source, large-scale, High-quality instruction tuning data that facilitates the construction of large language models with the ability to use common tools.
##Repository link: https://github.com/OpenBMB/ToolBenchThe ToolBench warehouse provides relevant data sets, training and evaluation scripts, and the functional model ToolLLaMA fine-tuned on ToolBench. The specific features are: 1. Supports single tool and multiple tools Tool solution The single tool setting follows the LangChain prompt style, and the multi-tool setting follows the AutoGPT prompt style. 2. The model reply not only includes the final answer, but also includes the model’s thinking chain process, tool execution and tool execution results 3. Support Real-world level complexity, supporting multi-step tool calls 4. Rich API that can be used for real-world scenarios such as weather information, search, stock updates, and PowerPoint automation 5. All data is automatically generated by the OpenAI API and filtered by the development team. The data creation process is easily scalable However It should be noted that the data released so far is not final, and researchers are still post-processing the data to improve data quality and increase the coverage of real-world tools. ToolBench The general idea of ToolBench is to train large language models in supervised data based on BMTools.
##The warehouse contains 9,800 pieces of data obtained from 312,000 real API calls, covering single Tool scenario and multi-tool scenario, the following is the statistical information of single tool.
Each row of data is a json dict, including the prompt template for data creation, Human instructions (queries) for tool usage, intermediate thought/tool execution loops, and final answers.
Tool Descrition:BMTools Tool_name: translationTool action: get_translationaction_input: {"text": target texts, "tgt_lang": target language}Generated Data:{"prompt": "Answer the following questions as best you can. Specifically, you have access to the following APIs:\n\nget_translation: . Your input should be a json (args json schema): {{\"text\" : string, \"tgt_lang\" : string, }} The Action to trigger this API should be get_translation and the input parameters should be a json dict string. Pay attention to the type of parameters.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [get_translation]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times, max 7 times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin! Remember: (1) Follow the format, i.e,\nThought:\nAction:\nAction Input:\nObservation:\nFinal Answer:\n (2) Provide as much as useful information in your Final Answer. (3) Do not make up anything, and if your Observation has no link, DO NOT hallucihate one. (4) If you have enough information and want to stop the process, please use \nThought: I have got enough information\nFinal Answer: **your response. \n The Action: MUST be one of the following:get_translation\nQuestion: {input}\n Agent scratchpad (history actions):\n {agent_scratchpad}","query": "My intention is to convert the data provided in ما هي الأقسام الثلاثة للقوات المسلحة؟ into Arabic(ara).\n","chains": [{"thought": "I need to use the get_translation API to convert the text into Arabic.","action": "get_translation","action_input": "{\"text\": \"What are the three branches of the military?\", \"tgt_lang\": \"ara\"}","observation": "\"ما هي الفروع الثلاثة للجيش ؟\""}],"answer": "The translation of \"What are the three branches of the military?\" into Arabic is \"ما هي الفروع الثلاثة للجيش ؟\"."}
Model experiment
Machine evaluation: The researchers randomly selected 100 chain steps for each tool steps) to build a machine evaluation test platform, with an average of 27 final steps and 73 intermediate tool call steps, where the final step is evaluated using the Rouge-L metric, and the intermediate step is evaluated using the ExactMatch metric.
Paper link: https://arxiv.org/pdf/2304.08354.pdf
The article also reviews existing tool learning research, including tool-enhanced and tool-oriented learning, and formulates a general tool learning framework: starting from understanding user instructions, the model should learn to decompose a complex task into Several subtasks, dynamically adapt the plan through reasoning, and conquer each subtask efficiently by choosing the right tool.
The article also discusses how to train models to improve tool usage and promote the popularization of tool learning.
Considering the lack of systematic tool learning evaluation in previous work, the researchers conducted experiments with 17 representative tools and demonstrated the performance of the current base model in skillfully utilizing the tools. potential.
The paper ends by discussing several open issues in tool learning that require further research, such as ensuring safe and trustworthy tool use, implementing tool creation with basic models, and solving personalization difficult problem.
Reference materials:
https://github.com/OpenBMB/ToolBench
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