The Synergy of Automated Pipelines with Prompt Engineering and Generative AI in Web Crawling
- URL: http://arxiv.org/abs/2502.15691v1
- Date: Sun, 29 Dec 2024 17:27:55 GMT
- Title: The Synergy of Automated Pipelines with Prompt Engineering and Generative AI in Web Crawling
- Authors: Chau-Jian Huang,
- Abstract summary: This study investigates the integration of generative AI tools Claude AI (Sonnet) and ChatGPT4.0 with prompt engineering to automate web scraping.<n>Claude AI consistently outperformed ChatGPT-4.0 in script quality and adaptability.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Web crawling is a critical technique for extracting online data, yet it poses challenges due to webpage diversity and anti-scraping mechanisms. This study investigates the integration of generative AI tools Claude AI (Sonnet 3.5) and ChatGPT4.0 with prompt engineering to automate web scraping. Using two prompts, PROMPT I (general inference, tested on Yahoo News) and PROMPT II (element-specific, tested on Coupons.com), we evaluate the code quality and performance of AI-generated scripts. Claude AI consistently outperformed ChatGPT-4.0 in script quality and adaptability, as confirmed by predefined evaluation metrics, including functionality, readability, modularity, and robustness. Performance data were collected through manual testing and structured scoring by three evaluators. Visualizations further illustrate Claude AI's superiority. Anti-scraping solutions, including undetected_chromedriver, Selenium, and fake_useragent, were incorporated to enhance performance. This paper demonstrates how generative AI combined with prompt engineering can simplify and improve web scraping workflows.
Related papers
- AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents [15.802600809497097]
This paper introduces AI2Agent, an end-to-end framework that automates AI project deployment through guideline-driven execution.
We conducted experiments on 30 AI deployment cases, covering TTS, text-to-image generation, image editing, and other AI applications.
Results show that AI2Agent significantly reduces deployment time and improves success rates.
arXiv Detail & Related papers (2025-03-31T10:58:34Z) - AIDetection: A Generative AI Detection Tool for Educators Using Syntactic Matching of Common ASCII Characters As Potential 'AI Traces' Within Users' Internet Browser [0.0]
AIDetection.info employs a syntactic-based approach to identify common traces left by generative AI models.
The tool scans documents in bulk for potential AI artifacts, as well as AI citations and acknowledgments, and provides a visual summary with downloadable Excel and CSV reports.
arXiv Detail & Related papers (2025-03-12T15:53:58Z) - General Scales Unlock AI Evaluation with Explanatory and Predictive Power [57.7995945974989]
benchmarking has guided progress in AI, but it has offered limited explanatory and predictive power for general-purpose AI systems.
We introduce general scales for AI evaluation that can explain what common AI benchmarks really measure.
Our fully-automated methodology builds on 18 newly-crafted rubrics that place instance demands on general scales that do not saturate.
arXiv Detail & Related papers (2025-03-09T01:13:56Z) - WebGames: Challenging General-Purpose Web-Browsing AI Agents [11.320069795732058]
WebGames is a comprehensive benchmark suite designed to evaluate general-purpose web-browsing AI agents.<n>We evaluate leading vision-language models including GPT-4o, Claude Computer-Use, Gemini-1.5-Pro, and Qwen2-VL against human performance.<n>Results reveal a substantial capability gap, with the best AI system achieving only 43.1% success rate compared to human performance of 95.7%.
arXiv Detail & Related papers (2025-02-25T16:45:08Z) - AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials [53.376263056033046]
Existing approaches rely on expensive human annotation, making them unsustainable at scale.<n>We propose AgentTrek, a scalable data synthesis pipeline that generates web agent trajectories by leveraging publicly available tutorials.<n>Our fully automated approach significantly reduces data collection costs, achieving a cost of just $0.55 per high-quality trajectory without human annotators.
arXiv Detail & Related papers (2024-12-12T18:59:27Z) - The BrowserGym Ecosystem for Web Agent Research [151.90034093362343]
BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents.<n>We propose an extended BrowserGym-based ecosystem for web agent research, which unifies existing benchmarks from the literature.<n>We conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across 6 popular web agent benchmarks.
arXiv Detail & Related papers (2024-12-06T23:43:59Z) - Adaptation of XAI to Auto-tuning for Numerical Libraries [0.0]
Explainable AI (XAI) technology is gaining prominence, aiming to streamline AI model development and alleviate the burden of explaining AI outputs to users.
This research focuses on XAI for AI models when integrated into two different processes for practical numerical computations.
arXiv Detail & Related papers (2024-05-12T09:00:56Z) - RoboScript: Code Generation for Free-Form Manipulation Tasks across Real
and Simulation [77.41969287400977]
This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation.
We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language.
We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
arXiv Detail & Related papers (2024-02-22T15:12:00Z) - A Preliminary Study on Using Large Language Models in Software
Pentesting [2.0551676463612636]
Large language models (LLM) are perceived to offer promising potentials for automating security tasks.
We investigate the use of LLMs in software pentesting, where the main task is to automatically identify software security vulnerabilities in source code.
arXiv Detail & Related papers (2024-01-30T21:42:59Z) - AI Content Self-Detection for Transformer-based Large Language Models [0.0]
This paper introduces the idea of direct origin detection and evaluates whether generative AI systems can recognize their output and distinguish it from human-written texts.
Google's Bard model exhibits the largest capability of self-detection with an accuracy of 94%, followed by OpenAI's ChatGPT with 83%.
arXiv Detail & Related papers (2023-12-28T10:08:57Z) - A Real-World WebAgent with Planning, Long Context Understanding, and
Program Synthesis [69.15016747150868]
We introduce WebAgent, an agent that learns from self-experience to complete tasks on real websites.
WebAgent plans ahead by decomposing instructions into canonical sub-instructions, summarizes long HTML documents into task-relevant snippets, and acts on websites.
We empirically demonstrate that our modular recipe improves the success on real websites by over 50%, and that HTML-T5 is the best model to solve various HTML understanding tasks.
arXiv Detail & Related papers (2023-07-24T14:56:30Z) - Comparing Software Developers with ChatGPT: An Empirical Investigation [0.0]
This paper conducts an empirical investigation, contrasting the performance of software engineers and AI systems, like ChatGPT, across different evaluation metrics.
The paper posits that a comprehensive comparison of software engineers and AI-based solutions, considering various evaluation criteria, is pivotal in fostering human-machine collaboration.
arXiv Detail & Related papers (2023-05-19T17:25:54Z) - AutoML-GPT: Automatic Machine Learning with GPT [74.30699827690596]
We propose developing task-oriented prompts and automatically utilizing large language models (LLMs) to automate the training pipeline.
We present the AutoML-GPT, which employs GPT as the bridge to diverse AI models and dynamically trains models with optimized hyper parameters.
This approach achieves remarkable results in computer vision, natural language processing, and other challenging areas.
arXiv Detail & Related papers (2023-05-04T02:09:43Z) - Fully Automated End-to-End Fake Audio Detection [57.78459588263812]
This paper proposes a fully automated end-toend fake audio detection method.
We first use wav2vec pre-trained model to obtain a high-level representation of the speech.
For the network structure, we use a modified version of the differentiable architecture search (DARTS) named light-DARTS.
arXiv Detail & Related papers (2022-08-20T06:46:55Z)
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.