An Exploratory Assessment of LLM's Potential Toward Flight Trajectory Reconstruction Analysis

• URL: http://arxiv.org/abs/2401.06204v1
• Date: Thu, 11 Jan 2024 17:59:18 GMT
• Title: An Exploratory Assessment of LLM's Potential Toward Flight Trajectory Reconstruction Analysis
• Authors: Qilei Zhang and John H. Mott
• Abstract summary: 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.
• Score: 3.3903227320938436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) hold transformative potential in aviation, particularly in reconstructing flight trajectories. This paper investigates this potential, grounded in the notion that LLMs excel at processing sequential data and deciphering complex data structures. Utilizing the LLaMA 2 model, a pre-trained open-source LLM, the study focuses on reconstructing flight trajectories using Automatic Dependent Surveillance-Broadcast (ADS-B) data with irregularities inherent in real-world scenarios. The findings demonstrate the model's proficiency in filtering noise and estimating both linear and curved flight trajectories. However, the analysis also reveals challenges in managing longer data sequences, which may be attributed to the token length limitations of LLM models. 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.

Related papers

• Evaluating the Effectiveness of Large Language Models in Representing and Understanding Movement Trajectories [1.3658544194443192]
  This research focuses on assessing the ability of AI foundation models in representing the trajectories of movements. We utilize one of the large language models (LLMs) to encode the string format of trajectories and then evaluate the effectiveness of the LLM-based representation for trajectory data analysis.
  arXiv  Detail & Related papers  (2024-08-31T02:57:25Z)
• Enhancing Temporal Understanding in LLMs for Semi-structured Tables [50.59009084277447]
  We conduct a comprehensive analysis of temporal datasets to pinpoint the specific limitations of large language models (LLMs) Our investigation leads to enhancements in TempTabQA, a dataset specifically designed for temporal temporal question answering. We introduce a novel approach, C.L.E.A.R. to strengthen LLM capabilities in this domain.
  arXiv  Detail & Related papers  (2024-07-22T20:13:10Z)
• PISTOL: Dataset Compilation Pipeline for Structural Unlearning of LLMs [31.16117964915814]
  Machine unlearning, which seeks to erase specific data stored in the pre-trained or fine-tuned models, has emerged as a crucial protective measure for LLMs. To facilitate the development of structural unlearning methods, we propose PISTOL, a pipeline for compiling multi-scenario datasets. We conduct benchmarks with four distinct unlearning methods on both Llama2-7B and Mistral-7B models.
  arXiv  Detail & Related papers  (2024-06-24T17:22:36Z)
• From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems [59.40480894948944]
  Large language model (LLM) empowered agents are able to solve decision-making problems in the physical world. Under this model, the LLM Planner navigates a partially observable Markov decision process (POMDP) by iteratively generating language-based subgoals via prompting. We prove that the pretrained LLM Planner effectively performs Bayesian aggregated imitation learning (BAIL) through in-context learning.
  arXiv  Detail & Related papers  (2024-05-30T09:42:54Z)
• Characterization of Large Language Model Development in the Datacenter [55.9909258342639]
  Large Language Models (LLMs) have presented impressive performance across several transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster resources to develop LLMs. We present an in-depth characterization study of a six-month LLM development workload trace collected from our GPU datacenter Acme.
  arXiv  Detail & Related papers  (2024-03-12T13:31:14Z)
• Are You Being Tracked? Discover the Power of Zero-Shot Trajectory Tracing with LLMs! [3.844253028598048]
  This study introduces LLMTrack, a model that illustrates how LLMs can be leveraged for Zero-Shot Trajectory Recognition. We evaluate the model using real-world datasets designed to challenge it with distinct trajectories characterized by indoor and outdoor scenarios.
  arXiv  Detail & Related papers  (2024-03-10T12:50:35Z)
• Towards Modeling Learner Performance with Large Language Models [7.002923425715133]
  This paper investigates whether the pattern recognition and sequence modeling capabilities of LLMs can be extended to the domain of knowledge tracing. We compare two approaches to using LLMs for this task, zero-shot prompting and model fine-tuning, with existing, non-LLM approaches to knowledge tracing. While LLM-based approaches do not achieve state-of-the-art performance, fine-tuned LLMs surpass the performance of naive baseline models and perform on par with standard Bayesian Knowledge Tracing approaches.
  arXiv  Detail & Related papers  (2024-02-29T14:06:34Z)
• Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks. The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
  arXiv  Detail & Related papers  (2024-01-30T17:38:54Z)
• Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning [79.32236399694077]
  Low-quality data in the training set are usually detrimental to instruction tuning. We propose a novel method, termed "reflection-tuning" This approach utilizes an oracle LLM to recycle the original training data by introspecting and enhancing the quality of instructions and responses in the data.
  arXiv  Detail & Related papers  (2023-10-18T05:13:47Z)
• TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models [52.734140807634624]
  Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs. We introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs.
  arXiv  Detail & Related papers  (2023-10-10T16:38:49Z)

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.