AI tasks encompass a wide range of domains and fields. While numerous AI models have been designed for specific tasks and applications, they often require considerable human efforts in finding the right model architecture, optimization algorithm, and hyperparameters. Recent advances in large language models (LLMs) like ChatGPT show remarkable capabilities in various aspects of reasoning, comprehension, and interaction. Consequently, we propose developing task-oriented prompts and automatically utilizing LLMs to automate the training pipeline. To implement this concept, we present the AutoML-GPT, which employs GPT as the bridge to diverse AI models and dynamically trains models with optimized hyperparameters. AutoML-GPT dynamically takes user requests from the model and data cards and composes the corresponding prompt paragraph. Ultimately, with this prompt paragraph, AutoML-GPT will automatically conduct the experiments from data processing to model architecture, hyperparameter tuning, and predicted training log. By leveraging {\ours}'s robust language capabilities and the available AI models, AutoML-GPT can tackle numerous intricate AI tasks across various tasks and datasets. This approach achieves remarkable results in computer vision, natural language processing, and other challenging areas. Extensive experiments and ablation studies demonstrate that our method can be general, effective, and beneficial for many AI tasks.
Using GPT-4 to Augment Unbalanced Data for Automatic Scoring
Franklin College of Arts and Sciences, University of Georgia（佐治亚大学富兰克林文理学院）；Mary Frances Early College of Education, University of Georgia（佐治亚大学玛丽·弗朗西斯·厄尔利教育学院）
Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）
Semantic features have been playing a central role in investigating the nature of our conceptual representations. Yet the enormous time and effort required to empirically sample and norm features from human raters has restricted their use to a limited set of manually curated concepts. Given recent promising developments with transformer-based language models, here we asked whether it was possible to use such models to automatically generate meaningful lists of properties for arbitrary object concepts and whether these models would produce features similar to those found in humans. To this end,…
FH Aachen University of Applied Sciences（亚琛应用科学大学）；ORDIX AG（ORDIX股份公司）
Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based approaches that promise to further improve automatic readability assessment. In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences. We combined these models with linguistic featu…
GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks
University of California, Santa Barbara（加州大学圣塔芭芭拉分校）；University of California, San Diego（加州大学圣迭戈分校）；University of Southern California（南加州大学）；Bytedance, US（字节跳动（美国））
In this work, we focus on fine-tuning an OpenAI GPT-2 pre-trained model for generating patent claims. GPT-2 has demonstrated impressive efficacy of pre-trained language models on various tasks, particularly coherent text generation. Patent claim language itself has rarely been explored in the past and poses a unique challenge. We are motivated to generate coherent patent claims automatically so that augmented inventing might be viable someday. In our implementation, we identified a unique language structure in patent claims and leveraged its implicit human annotations. We investigated the fine…
An automatically discovered chain-of-thought prompt generalizes to novel models and datasets
Medical University of Vienna（维也纳医科大学）；Johannes Kepler University Linz（林茨约翰内斯·开普勒大学）；KTH Royal Institute of Technology（KTH皇家理工学院）；Humboldt University Berlin（柏林洪堡大学）
University of Texas at Austin（得克萨斯大学奥斯汀分校）；Ulsan National Institute of Science and Technology（蔚山国家科学技术研究院）；Korea Institute of Science and Technology (KIST) Europe（韩国科学技术研究院欧洲分院）
Automated generation of scientific protocols executable by robots can significantly accelerate scientific research processes. Large Language Models (LLMs) excel at Scientific Protocol Formulation Tasks (SPFT), but the evaluation of their capabilities rely on human evaluation. Here, we propose a flexible, automatic framework to evaluate LLMs' capability on SPFT: ProtoMed-LLM. This framework prompts the target model and GPT-4 to extract pseudocode from biology protocols using only predefined lab actions and evaluates the output of the target model using LLAM-EVAL, the pseudocode generated by GPT…
Can GPT-4 Help Detect Quit Vaping Intentions? An Exploration of Automatic Data Annotation Approach
HI3 Tech Lab, University of South Carolina（南卡罗来纳大学HI3技术实验室）；School of Medicine, Washington University（华盛顿大学医学院）；University of Texas Health Science Center（德克萨斯大学健康科学中心）
Rapidly Developing High-quality Instruction Data and Evaluation Benchmark for Large Language Models with Minimal Human Effort: A Case Study on Japanese
Thomas Jefferson University（托马斯杰斐逊大学）；Weil-Cornell Medical Center（威尔康奈尔医学中心）；The Ohio State University（俄亥俄州立大学）；The Jackson Lab（杰克逊实验室）；Unity Health（联合健康）
Purpose: The purpose of this study was to determine if an ensemble of multiple LLM agents could be used collectively to provide a more reliable assessment of a pixel-based AI triage tool than a single LLM. Methods: 29,766 non-contrast CT head exams from fourteen hospitals were processed by a commercial intracranial hemorrhage (ICH) AI detection tool. Radiology reports were analyzed by an ensemble of eight open-source LLM models and a HIPAA compliant internal version of GPT-4o using a single multi-shot prompt that assessed for presence of ICH. 1,726 examples were manually reviewed. Performance …
Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems
Department of Computer Science, College of Computing and Mathematical Sciences, Khalifa University（计算机科学系，计算与数学科学学院，哈利法大学）；ECE Department, University of Toronto（电子工程系，多伦多大学）；Concordia Institute for Information Systems Engineering (CIISE), Concordia University（信息系统工程研究所（CIISE），康科迪亚大学）；Department of Electrical Engineering, College of Engineering and Physical Sciences, Khalifa University（电气工程系，工程与物理科学学院，哈利法大学）
Key Laboratory of Machine Perception, MOE, School of EECS, Peking University（北京大学信息科学技术学院机器感知与智能教育部重点实验室）；Peking University（北京大学）；Microsoft Research Asia（微软亚洲研究院）；Microsoft（微软）
Large-scale pre-trained models have attracted extensive attention in the research community and shown promising results on various tasks of natural language processing. However, these pre-trained models are memory and computation intensive, hindering their deployment into industrial online systems like Ad Relevance. Meanwhile, how to design an effective yet efficient model architecture is another challenging problem in online Ad Relevance. Recently, AutoML shed new lights on architecture design, but how to integrate it with pre-trained language models remains unsettled. In this paper, we propo…
This study is devoted to the automatic generation of German drama texts. We suggest an approach consisting of two key steps: fine-tuning a GPT-2 model (the outline model) to generate outlines of scenes based on keywords and fine-tuning a second model (the generation model) to generate scenes from the scene outline. The input for the neural model comprises two datasets: the German Drama Corpus (GerDraCor) and German Text Archive (Deutsches Textarchiv or DTA). In order to estimate the effectiveness of the proposed method, our models are compared with baseline GPT-2 models. Our models perform wel…
With the COVID-19 pandemic, there is a growing urgency for medical community to keep up with the accelerating growth in the new coronavirus-related literature. As a result, the COVID-19 Open Research Dataset Challenge has released a corpus of scholarly articles and is calling for machine learning approaches to help bridging the gap between the researchers and the rapidly growing publications. Here, we take advantage of the recent advances in pre-trained NLP models, BERT and OpenAI GPT-2, to solve this challenge by performing text summarization on this dataset. We evaluate the results using ROU…
While specifications for digital systems are provided in natural language, engineers undertake significant efforts to translate them into the programming languages understood by compilers for digital systems. Automating this process allows designers to work with the language in which they are most comfortable --the original natural language -- and focus instead on other downstream design challenges. We explore the use of state-of-the-art machine learning (ML) to automatically derive Verilog snippets from English via fine-tuning GPT-2, a natural language ML system. We describe our approach for …
Language models (LMs) pre-trained on massive amounts of text, in particular bidirectional encoder representations from Transformers (BERT), generative pre-training (GPT), and GPT-2, have become a key technology for many natural language processing tasks. In this paper, we present results using fine-tuned GPT, GPT-2, and their combination for automatic speech recognition (ASR). Unlike unidirectional LM GPT and GPT-2, BERT is bidirectional whose direct product of the output probabilities is no longer a valid language prior probability. A conversion method is proposed to compute the correct langu…
