Related papers: Professional Certification Benchmark Dataset: The First 500 Jobs For Large Language Models

Professional Certification Benchmark Dataset: The First 500 Jobs For Large Language Models

URL: http://arxiv.org/abs/2305.05377v1
Date: Sun, 7 May 2023 00:56:58 GMT
Title: Professional Certification Benchmark Dataset: The First 500 Jobs For Large Language Models
Authors: David Noever and Matt Ciolino
Abstract summary: The research creates a professional certification survey to test large language models and evaluate their employable skills. It compares the performance of two AI models, GPT-3 and Turbo-GPT3.5, on a benchmark dataset of 1149 professional certifications.
Score: 0.0
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: The research creates a professional certification survey to test large language models and evaluate their employable skills. It compares the performance of two AI models, GPT-3 and Turbo-GPT3.5, on a benchmark dataset of 1149 professional certifications, emphasizing vocational readiness rather than academic performance. GPT-3 achieved a passing score (>70% correct) in 39% of the professional certifications without fine-tuning or exam preparation. The models demonstrated qualifications in various computer-related fields, such as cloud and virtualization, business analytics, cybersecurity, network setup and repair, and data analytics. Turbo-GPT3.5 scored 100% on the valuable Offensive Security Certified Professional (OSCP) exam. The models also displayed competence in other professional domains, including nursing, licensed counseling, pharmacy, and teaching. Turbo-GPT3.5 passed the Financial Industry Regulatory Authority (FINRA) Series 6 exam with a 70% grade without preparation. Interestingly, Turbo-GPT3.5 performed well on customer service tasks, suggesting potential applications in human augmentation for chatbots in call centers and routine advice services. The models also score well on sensory and experience-based tests such as wine sommelier, beer taster, emotional quotient, and body language reader. The OpenAI model improvement from Babbage to Turbo resulted in a median 60% better-graded performance in less than a few years. This progress suggests that focusing on the latest model's shortcomings could lead to a highly performant AI capable of mastering the most demanding professional certifications. We open-source the benchmark to expand the range of testable professional skills as the models improve or gain emergent capabilities.

Related papers

Unearthing Skill-Level Insights for Understanding Trade-Offs of Foundation Models [61.467781476005435]
skill-wise performance is obscured when inspecting aggregate accuracy, under-utilizing the rich signal modern benchmarks contain. We propose an automatic approach to recover the underlying skills relevant for any evaluation instance, by way of inspecting model-generated rationales. Our skill-slices and framework open a new avenue in model evaluation, leveraging skill-specific analyses to unlock a more granular and actionable understanding of model capabilities.
arXiv Detail & Related papers (2024-10-17T17:51:40Z)
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants [175.9723801486487]
We evaluate whether two AI assistants, GPT-3.5 and GPT-4, can adequately answer assessment questions. GPT-4 answers an average of 65.8% of questions correctly, and can even produce the correct answer across at least one prompting strategy for 85.1% of questions. Our results call for revising program-level assessment design in higher education in light of advances in generative AI.
arXiv Detail & Related papers (2024-08-07T12:11:49Z)
Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking [59.87055275344965]
Job-SDF is a dataset designed to train and benchmark job-skill demand forecasting models. Based on 10.35 million public job advertisements collected from major online recruitment platforms in China between 2021 and 2023. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels.
arXiv Detail & Related papers (2024-06-17T07:22:51Z)
GPT-4 passes most of the 297 written Polish Board Certification Examinations [0.5461938536945723]
This study evaluated the performance of three Generative Pretrained Transformer (GPT) models on the Polish Board Certification Exam (Pa'nstwowy Egzamin Specjalizacyjny, PES) dataset. The GPT models varied significantly, displaying excellence in exams related to certain specialties while completely failing others.
arXiv Detail & Related papers (2024-04-29T09:08:22Z)
Evaluating AI Vocational Skills Through Professional Testing [0.0]
The study focuses on assessing the vocational skills of two AI models, GPT-3 and Turbo-GPT3.5. Both models scored well on sensory and experience-based tests outside a machine's traditional roles. The study found that OpenAI's model improvement from Babbage to Turbo led to a 60% better performance on the grading scale within a few years.
arXiv Detail & Related papers (2023-12-17T04:41:59Z)
Fine-tuning ChatGPT for Automatic Scoring [1.4833692070415454]
This study highlights the potential of fine-tuned ChatGPT (GPT3.5) for automatically scoring student written constructed responses. We compare the performance of fine-tuned GPT-3.5 with the fine-tuned state-of-the-art Google's generated language model, BERT.
arXiv Detail & Related papers (2023-10-16T05:09:16Z)
Performance of Large Language Models in a Computer Science Degree Program [0.5330240017302619]
This paper presents findings on the performance of different large language models in a university of applied sciences' undergraduate computer science degree program. By prompting the models with lecture material, exercise tasks, and past exams, we aim to evaluate their proficiency across different computer science domains. We found that ChatGPT-3.5 averaged 79.9% of the total score in 10 tested modules, BingAI achieved 68.4%, and LLaMa, in the 65 billion parameter variant, 20%.
arXiv Detail & Related papers (2023-07-24T14:17:00Z)
AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models [122.63704560157909]
We introduce AGIEval, a novel benchmark designed to assess foundation model in the context of human-centric standardized exams. We evaluate several state-of-the-art foundation models, including GPT-4, ChatGPT, and Text-Davinci-003. GPT-4 surpasses average human performance on SAT, LSAT, and math competitions, attaining a 95% accuracy rate on the SAT Math test and a 92.5% accuracy on the English test of the Chinese national college entrance exam.
arXiv Detail & Related papers (2023-04-13T09:39:30Z)
Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification [58.720142291102135]
This case study investigates the task of job classification in a real-world setting. The goal is to determine whether an English-language job posting is appropriate for a graduate or entry-level position.
arXiv Detail & Related papers (2023-03-13T14:09:53Z)
How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks [65.7949334650854]
GPT-3.5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks. However, their robustness and abilities to handle various complexities of the open world have yet to be explored. We show that GPT-3.5 faces some specific robustness challenges, including instability, prompt sensitivity, and number sensitivity.
arXiv Detail & Related papers (2023-03-01T07:39:01Z)
GPT Takes the Bar Exam [0.0]
We document our experimental evaluation of the performance of OpenAI's text-davinci-003 model, often-referred to as GPT-3.5. For best prompt and parameters, GPT-3.5 achieves a headline correct rate of 50.3% on a complete NCBE MBE practice exam.
arXiv Detail & Related papers (2022-12-29T18:19:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.