RestGPT: Connecting Large Language Models with Real-World RESTful APIs

• URL: http://arxiv.org/abs/2306.06624v2
• Date: Sun, 27 Aug 2023 02:55:36 GMT
• Title: RestGPT: Connecting Large Language Models with Real-World RESTful APIs
• Authors: Yifan Song, Weimin Xiong, Dawei Zhu, Wenhao Wu, Han Qian, Mingbo Song, Hailiang Huang, Cheng Li, Ke Wang, Rong Yao, Ye Tian, Sujian Li
• Abstract summary: A tool-augmented large language models (LLMs) have achieved remarkable progress in tackling a broad range of tasks. To address the practical challenges of tackling complex instructions, we propose RestGPT, which exploits the power of robustness. To fully evaluate RestGPT, we propose RestBench, a high-quality benchmark which consists of two real-world scenarios and human-annotated instructions.
• Score: 44.94234920380684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tool-augmented large language models (LLMs) have achieved remarkable progress in tackling a broad range of tasks. However, existing methods are mainly restricted to specifically designed tools and fail to fulfill complex instructions, having great limitations when confronted with real-world scenarios. In this paper, we explore a more realistic scenario by connecting LLMs with RESTful APIs, which adhere to the widely adopted REST software architectural style for web service development. To address the practical challenges of tackling complex instructions, we propose RestGPT, which exploits the power of LLMs and conducts a coarse-to-fine online planning mechanism to enhance the abilities of task decomposition and API selection. RestGPT also contains an API executor tailored for calling RESTful APIs, which can meticulously formulate parameters and parse API responses. To fully evaluate the performance of RestGPT, we propose RestBench, a high-quality benchmark which consists of two real-world scenarios and human-annotated instructions with gold solution paths. Experiments show that RestGPT is able to achieve impressive results in complex tasks and has strong robustness, which paves a new way towards AGI. RestGPT and RestBench is publicly available at https://restgpt.github.io/.

Related papers

• SEAL: Suite for Evaluating API-use of LLMs [1.2528321519119252]
  SEAL is an end-to-end testbed designed to evaluate large language models in real-world API usage. It standardizes existing benchmarks, integrates an agent system for testing API retrieval and planning, and addresses the instability of real-time APIs.
  arXiv  Detail & Related papers  (2024-09-23T20:16:49Z)
• FANTAstic SEquences and Where to Find Them: Faithful and Efficient API Call Generation through State-tracked Constrained Decoding and Reranking [57.53742155914176]
  API call generation is the cornerstone of large language models' tool-using ability. Existing supervised and in-context learning approaches suffer from high training costs, poor data efficiency, and generated API calls that can be unfaithful to the API documentation and the user's request. We propose an output-side optimization approach called FANTASE to address these limitations.
  arXiv  Detail & Related papers  (2024-07-18T23:44:02Z)
• ShortcutsBench: A Large-Scale Real-world Benchmark for API-based Agents [7.166156709980112]
  We introduce textscShortcutsBench, a large-scale benchmark for the comprehensive evaluation of API-based agents. textscShortcutsBench includes a wealth of real APIs from Apple Inc.'s operating systems. Our evaluation reveals significant limitations in handling complex queries related to API selection, parameter filling, and requesting necessary information from systems and users.
  arXiv  Detail & Related papers  (2024-06-28T08:45:02Z)
• You Can REST Now: Automated Specification Inference and Black-Box Testing of RESTful APIs with Large Language Models [8.753312212588371]
  manually documenting APIs is a time-consuming and error-prone task, resulting in unavailable, incomplete, or imprecise documentation. Recently, Large Language Models (LLMs) have demonstrated exceptional abilities to automate tasks based on their colossal training data. We present RESTSpecIT, the first automated API specification inference and black-box testing approach.
  arXiv  Detail & Related papers  (2024-02-07T18:55:41Z)
• Leveraging Large Language Models to Improve REST API Testing [51.284096009803406]
  RESTGPT takes as input an API specification, extracts machine-interpretable rules, and generates example parameter values from natural-language descriptions in the specification. Our evaluations indicate that RESTGPT outperforms existing techniques in both rule extraction and value generation.
  arXiv  Detail & Related papers  (2023-12-01T19:53:23Z)
• 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)
• Adaptive REST API Testing with Reinforcement Learning [54.68542517176757]
  Current testing tools lack efficient exploration mechanisms, treating all operations and parameters equally. Current tools struggle when response schemas are absent in the specification or exhibit variants. We present an adaptive REST API testing technique incorporates reinforcement learning to prioritize operations during exploration.
  arXiv  Detail & Related papers  (2023-09-08T20:27:05Z)
• ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs [104.37772295581088]
  Open-source large language models (LLMs), e.g., LLaMA, remain significantly limited in tool-use capabilities. We introduce ToolLLM, a general tool-usetuning encompassing data construction, model training, and evaluation. We first present ToolBench, an instruction-tuning framework for tool use, which is constructed automatically using ChatGPT.
  arXiv  Detail & Related papers  (2023-07-31T15:56:53Z)

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.