AviationGPT: A Large Language Model for the Aviation Domain
- URL: http://arxiv.org/abs/2311.17686v1
- Date: Wed, 29 Nov 2023 14:49:31 GMT
- Title: AviationGPT: A Large Language Model for the Aviation Domain
- Authors: Liya Wang, Jason Chou, Xin Zhou, Alex Tien, Diane M Baumgartner
- Abstract summary: AviationGPT is built on open-source LLaMA-2 and Mistral architectures and continuously trained on a wealth of carefully curated aviation datasets.
It offers users multiple advantages, including the versatility to tackle diverse natural language processing (NLP) problems.
It also provides accurate and contextually relevant responses within the aviation domain.
- Score: 4.010674039471089
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The advent of ChatGPT and GPT-4 has captivated the world with large language
models (LLMs), demonstrating exceptional performance in question-answering,
summarization, and content generation. The aviation industry is characterized
by an abundance of complex, unstructured text data, replete with technical
jargon and specialized terminology. Moreover, labeled data for model building
are scarce in this domain, resulting in low usage of aviation text data. The
emergence of LLMs presents an opportunity to transform this situation, but
there is a lack of LLMs specifically designed for the aviation domain. To
address this gap, we propose AviationGPT, which is built on open-source LLaMA-2
and Mistral architectures and continuously trained on a wealth of carefully
curated aviation datasets. Experimental results reveal that AviationGPT offers
users multiple advantages, including the versatility to tackle diverse natural
language processing (NLP) problems (e.g., question-answering, summarization,
document writing, information extraction, report querying, data cleaning, and
interactive data exploration). It also provides accurate and contextually
relevant responses within the aviation domain and significantly improves
performance (e.g., over a 40% performance gain in tested cases). With
AviationGPT, the aviation industry is better equipped to address more complex
research problems and enhance the efficiency and safety of National Airspace
System (NAS) operations.
Related papers
- BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages [93.92804151830744]
We present BRIGHTER, a collection of emotion-annotated datasets in 28 different languages.
We describe the data collection and annotation processes and the challenges of building these datasets.
We show that BRIGHTER datasets are a step towards bridging the gap in text-based emotion recognition.
arXiv Detail & Related papers (2025-02-17T15:39:50Z) - LLM Evaluation Based on Aerospace Manufacturing Expertise: Automated Generation and Multi-Model Question Answering [5.426193610598865]
This paper introduces a set of evaluation metrics tailored for Large Language Models (LLMs) in aerospace manufacturing.
Key information is extracted through in-depth textual analysis of classic aerospace manufacturing textbooks and guidelines.
We meticulously construct multiple-choice questions with multiple correct answers of varying difficulty.
Different LLM models are employed to answer these questions, and their accuracy is recorded.
arXiv Detail & Related papers (2025-01-25T12:26:44Z) - Towards Human-Guided, Data-Centric LLM Co-Pilots [53.35493881390917]
CliMB-DC is a human-guided, data-centric framework for machine learning co-pilots.
It combines advanced data-centric tools with LLM-driven reasoning to enable robust, context-aware data processing.
We show how CliMB-DC can transform uncurated datasets into ML-ready formats.
arXiv Detail & Related papers (2025-01-17T17:51:22Z) - Applications of natural language processing in aviation safety: A review and qualitative analysis [0.0]
This study explores using Natural Language Processing in aviation safety.
It focuses on machine learning algorithms to enhance safety measures.
There are currently 34 Scopus results from the keyword search natural language processing and aviation safety.
arXiv Detail & Related papers (2025-01-03T07:36:10Z) - Aerial Vision-and-Language Navigation via Semantic-Topo-Metric Representation Guided LLM Reasoning [48.33405770713208]
We propose an end-to-end framework for aerial VLN tasks, where the large language model (LLM) is introduced as our agent for action prediction.
We develop a novel Semantic-Topo-Metric Representation (STMR) to enhance the spatial reasoning ability of LLMs.
Experiments conducted in real and simulation environments have successfully proved the effectiveness and robustness of our method.
arXiv Detail & Related papers (2024-10-11T03:54:48Z) - DSBench: How Far Are Data Science Agents to Becoming Data Science Experts? [58.330879414174476]
We introduce DSBench, a benchmark designed to evaluate data science agents with realistic tasks.
This benchmark includes 466 data analysis tasks and 74 data modeling tasks, sourced from Eloquence and Kaggle competitions.
Our evaluation of state-of-the-art LLMs, LVLMs, and agents shows that they struggle with most tasks, with the best agent solving only 34.12% of data analysis tasks and achieving a 34.74% Relative Performance Gap (RPG)
arXiv Detail & Related papers (2024-09-12T02:08:00Z) - An Exploratory Assessment of LLM's Potential Toward Flight Trajectory
Reconstruction Analysis [3.3903227320938436]
The study focuses on reconstructing flight trajectories using Automatic Dependent Surveillance-Broadcast (ADS-B) data.
The findings demonstrate the model's proficiency in filtering noise and estimating both linear and curved flight trajectories.
The study's insights underscore the promise of LLMs in flight trajectory reconstruction and open new avenues for their broader application across the aviation and transportation sectors.
arXiv Detail & Related papers (2024-01-11T17:59:18Z) - KITLM: Domain-Specific Knowledge InTegration into Language Models for
Question Answering [30.129418454426844]
Large language models (LLMs) have demonstrated remarkable performance in a wide range of natural language tasks.
We propose, KITLM, a novel knowledge base integration approach into language model through relevant information infusion.
Our proposed knowledge-infused model surpasses the performance of both GPT-3.5-turbo and the state-of-the-art knowledge infusion method, SKILL, achieving over 1.5 times improvement in exact match scores on the MetaQA.
arXiv Detail & Related papers (2023-08-07T14:42:49Z) - Adapting Sentence Transformers for the Aviation Domain [0.8437187555622164]
We propose a novel approach for adapting sentence transformers for the aviation domain.
Our method is a two-stage process consisting of pre-training followed by fine-tuning.
Our work highlights the importance of domain-specific adaptation in developing high-quality NLP solutions for specialized industries like aviation.
arXiv Detail & Related papers (2023-05-16T15:53:24Z) - Improving aircraft performance using machine learning: a review [57.82442188072833]
This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering.
We review the state of the art, gathering the advantages and challenges of ML methods across different aerospace disciplines.
arXiv Detail & Related papers (2022-10-20T07:16:53Z) - Data-Driven Aerospace Engineering: Reframing the Industry with Machine
Learning [49.367020832638794]
The aerospace industry is poised to capitalize on big data and machine learning.
Recent trends will be explored in context of critical challenges in design, manufacturing, verification and services.
arXiv Detail & Related papers (2020-08-24T22:40:26Z)
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.