Improving the Learnability of Machine Learning APIs by Semi-Automated API Wrapping

• URL: http://arxiv.org/abs/2203.15491v1
• Date: Tue, 29 Mar 2022 12:42:05 GMT
• Title: Improving the Learnability of Machine Learning APIs by Semi-Automated API Wrapping
• Authors: Lars Reimann, G\"unter Kniesel-W\"unsche
• Abstract summary: We address the challenge of creating APIs that are easy to learn and use, especially by novices. We investigate this problem for skl, a widely used ML API. We identify unused and apparently useless parts of the API that can be eliminated without affecting client programs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A major hurdle for students and professional software developers who want to enter the world of machine learning (ML), is mastering not just the scientific background but also the available ML APIs. Therefore, we address the challenge of creating APIs that are easy to learn and use, especially by novices. However, it is not clear how this can be achieved without compromising expressiveness. We investigate this problem for \skl{}, a widely used ML API. In this paper, we analyze its use by the Kaggle community, identifying unused and apparently useless parts of the API that can be eliminated without affecting client programs. In addition, we discuss usability issues in the remaining parts, propose related design improvements and show how they can be implemented by semi-automated wrapping of the existing third-party API.

Related papers

• Demystifying Application Programming Interfaces (APIs): Unlocking the Power of Large Language Models and Other Web-based AI Services in Social Work Research [0.0]
  Application Programming Interfaces (APIs) are essential tools for social work researchers aiming to harness advanced technologies like Large Language Models (LLMs) and other AI services. This paper demystifies APIs and illustrates how they can enhance research methodologies. Practical code examples demonstrate how LLMs can generate API code for accessing specialized services, such as extracting data from unstructured text.
  arXiv  Detail & Related papers  (2024-10-26T16:07:12Z)
• A Systematic Evaluation of Large Code Models in API Suggestion: When, Which, and How [53.65636914757381]
  API suggestion is a critical task in modern software development. Recent advancements in large code models (LCMs) have shown promise in the API suggestion task.
  arXiv  Detail & Related papers  (2024-09-20T03:12:35Z)
• WorldAPIs: The World Is Worth How Many APIs? A Thought Experiment [49.00213183302225]
  We propose a framework to induce new APIs by grounding wikiHow instruction to situated agent policies. Inspired by recent successes in large language models (LLMs) for embodied planning, we propose a few-shot prompting to steer GPT-4.
  arXiv  Detail & Related papers  (2024-07-10T15:52:44Z)
• A Solution-based LLM API-using Methodology for Academic Information Seeking [49.096714812902576]
  SoAy is a solution-based LLM API-using methodology for academic information seeking. It uses code with a solution as the reasoning method, where a solution is a pre-constructed API calling sequence. Results show a 34.58-75.99% performance improvement compared to state-of-the-art LLM API-based baselines.
  arXiv  Detail & Related papers  (2024-05-24T02:44:14Z)
• SpeCrawler: Generating OpenAPI Specifications from API Documentation Using Large Language Models [8.372941103284774]
  SpeCrawler is a comprehensive system that generates OpenAPI Specifications from diverse API documentation. The paper explores SpeCrawler's methodology, supported by empirical evidence and case studies.
  arXiv  Detail & Related papers  (2024-02-18T15:33:24Z)
• APICom: Automatic API Completion via Prompt Learning and Adversarial Training-based Data Augmentation [6.029137544885093]
  API recommendation is the process of assisting developers in finding the required API among numerous candidate APIs. Previous studies mainly modeled API recommendation as the recommendation task, and developers may not yet be able to find what they need. Motivated by the neural machine translation research domain, we can model this problem as the generation task. We propose a novel approach APICom based on prompt learning, which can generate API related to the query according to the prompts.
  arXiv  Detail & Related papers  (2023-09-13T15:31:50Z)
• 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)
• Private-Library-Oriented Code Generation with Large Language Models [52.73999698194344]
  This paper focuses on utilizing large language models (LLMs) for code generation in private libraries. We propose a novel framework that emulates the process of programmers writing private code. We create four private library benchmarks, including TorchDataEval, TorchDataComplexEval, MonkeyEval, and BeatNumEval.
  arXiv  Detail & Related papers  (2023-07-28T07:43:13Z)
• API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs [84.45284695156771]
  API-Bank is a groundbreaking benchmark for tool-augmented Large Language Models. We develop a run evaluation system consisting of 73 API tools. We construct a comprehensive training set containing 1,888 tool-use dialogues from 2,138 APIs spanning 1,000 distinct domains.
  arXiv  Detail & Related papers  (2023-04-14T14:05:32Z)
• HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions [35.48276161473216]
  We present HAPI, a longitudinal dataset of 1,761,417 instances of commercial ML API applications. Each instance consists of a query input for an API along with the API's output prediction/annotation and confidence scores.
  arXiv  Detail & Related papers  (2022-09-18T01:52:16Z)
• On the Effectiveness of Pretrained Models for API Learning [8.788509467038743]
  Developers frequently use APIs to implement certain functionalities, such as parsing Excel Files, reading and writing text files line by line, etc. Developers can greatly benefit from automatic API usage sequence generation based on natural language queries for building applications in a faster and cleaner manner. Existing approaches utilize information retrieval models to search for matching API sequences given a query or use RNN-based encoder-decoder to generate API sequences.
  arXiv  Detail & Related papers  (2022-04-05T20:33:24Z)

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.