Chattronics: using GPTs to assist in the design of data acquisition systems

• URL: http://arxiv.org/abs/2409.15183v1
• Date: Mon, 23 Sep 2024 16:36:16 GMT
• Title: Chattronics: using GPTs to assist in the design of data acquisition systems
• Authors: Jonathan Paul Driemeyer Brown, Tiago Oliveira Weber,
• Abstract summary: This article presents a novel approach to use General Pre-Trained Transformers to assist in the design phase of data acquisition systems. The solution is packaged in the form of an application that retains the conversational aspects of LLMs. After 160 test iterations, the study concludes that there is potential for these models to serve adequately as synthesis/assistant tools for data acquisition systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The usefulness of Large Language Models (LLM) is being continuously tested in various fields. However, their intrinsic linguistic characteristic is still one of the limiting factors when applying these models to exact sciences. In this article, a novel approach to use General Pre-Trained Transformers to assist in the design phase of data acquisition systems will be presented. The solution is packaged in the form of an application that retains the conversational aspects of LLMs, in such a manner that the user must provide details on the desired project in order for the model to draft both a system-level architectural diagram and the block-level specifications, following a Top-Down methodology based on restrictions. To test this tool, two distinct user emulations were used, one of which uses an additional GPT model. In total, 4 different data acquisition projects were used in the testing phase, each with its own measurement requirements: angular position, temperature, acceleration and a fourth project with both pressure and superficial temperature measurements. After 160 test iterations, the study concludes that there is potential for these models to serve adequately as synthesis/assistant tools for data acquisition systems, but there are still technological limitations. The results show coherent architectures and topologies, but that GPTs have difficulties in simultaneously considering all requirements and many times commits theoretical mistakes.

Related papers

• ToolACE: Winning the Points of LLM Function Calling [139.07157814653638]
  ToolACE is an automatic agentic pipeline designed to generate accurate, complex, and diverse tool-learning data. We demonstrate that models trained on our synthesized data, even with only 8B parameters, achieve state-of-the-art performance on the Berkeley Function-Calling Leaderboard.
  arXiv  Detail & Related papers  (2024-09-02T03:19:56Z)
• Experimenting with Multi-Agent Software Development: Towards a Unified Platform [3.3485481369444674]
  Large language models are redefining software engineering by implementing AI-powered techniques throughout the whole software development process. This study is to develop a unified platform that utilizes multiple artificial intelligence agents to automate the process of transforming user requirements into well-organized deliverables. The platform will organize tasks, perform security and compliance, and suggest design patterns and improvements for non-functional requirements.
  arXiv  Detail & Related papers  (2024-06-08T07:27:01Z)
• TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data [73.29220562541204]
  We consider harnessing the amazing power of language models (LLMs) to solve our task. We develop a TAT-LLM language model by fine-tuning LLaMA 2 with the training data generated automatically from existing expert-annotated datasets.
  arXiv  Detail & Related papers  (2024-01-24T04:28:50Z)
• Code Soliloquies for Accurate Calculations in Large Language Models [22.1024285108075]
  High-quality conversational datasets are crucial for the successful development of Intelligent Tutoring Systems. These datasets are generated using advanced GPT-4 models. Our design orchestrates a mock conversation where both student and tutorbot roles are simulated by GPT-4. Our approach notably enhances the quality of synthetic conversation datasets, especially for subjects that are calculation-intensive.
  arXiv  Detail & Related papers  (2023-09-21T15:16:58Z)
• The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute [66.84421705029624]
  We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours. We pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length. This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput.
  arXiv  Detail & Related papers  (2023-09-20T10:31:17Z)
• AutoML-GPT: Large Language Model for AutoML [5.9145212342776805]
  We have established a framework called AutoML-GPT that integrates a comprehensive set of tools and libraries. Through a conversational interface, users can specify their requirements, constraints, and evaluation metrics. We have demonstrated that AutoML-GPT significantly reduces the time and effort required for machine learning tasks.
  arXiv  Detail & Related papers  (2023-09-03T09:39:49Z)
• Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task [49.50140712943701]
  We evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples. We also conduct a comprehensive investigation on ChatGPT/GPT-4's reasoning ability by introducing varying levels of inference difficulty.
  arXiv  Detail & Related papers  (2023-04-18T17:21:48Z)
• Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design [10.531935694354448]
  This paper doubles as a review and practical guide to evaluation metrics for deep generative models (DGMs) in engineering design. We first summarize the well-accepted classic' evaluation metrics for deep generative models grounded in machine learning theory. Next, we curate a set of design-specific metrics which can be used for evaluating deep generative models.
  arXiv  Detail & Related papers  (2023-02-06T16:34:16Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems [74.8759568242933]
  Task-oriented dialogue systems use four connected modules, namely, Natural Language Understanding (NLU), a Dialogue State Tracking (DST), Dialogue Policy (DP) and Natural Language Generation (NLG) A research challenge is to learn each module with the least amount of samples given the high cost related to the data collection. We evaluate the priming few-shot ability of language models in the NLU, DP and NLG tasks.
  arXiv  Detail & Related papers  (2020-08-14T08:23:21Z)

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.