mlsanityData to 14 Oct 2026

Knots: A Large-Scale Multi-Agent Enhanced Expert-Annotated Dataset and LLM Prompt Optimization for NOTAM Semantic Parsing

arXiv 2511.12630 ↗First posted 16 Nov 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

Notice to Air Missions (NOTAMs) serve as a critical channel for disseminating key flight safety information, yet their complex linguistic structures and implicit reasoning pose significant challenges for automated parsing. Existing research mainly focuses on surface-level tasks such as classification and named entity recognition, lacking deep semantic understanding. To address this gap, we propose NOTAM semantic parsing, a task emphasizing semantic inference and the integration of aviation domain knowledge to produce structured, inference-rich outputs. To support this task, we construct Knots (Knowledge and NOTAM Semantics), a high-quality dataset of 12,347 expert-annotated NOTAMs covering 194 Flight Information Regions, enhanced through a multi-agent collaborative framework for comprehensive field discovery. We systematically evaluate a wide range of prompt-engineering strategies and model-adaptation techniques, achieving substantial improvements in aviation text understanding and processing. Our experimental results demonstrate the effectiveness of the proposed approach and offer valuable insights for automated NOTAM analysis systems. Our code is available at: https://github.com/Estrellajer/Knots.

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References · 30

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
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  10. 2022Automatic Chain of Thought Prompting in Large Language Modelscited by 688
  11. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 431
  12. 2025Multi-Agent Collaboration Mechanisms: A Survey of LLMscited by 431
18 more
  1. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 324
  2. 2023Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology Viewcited by 222
  3. 2023Large Language Models for Generative Information Extraction: A Surveycited by 185
  4. 2023InstructUIE: Multi-task Instruction Tuning for Unified Information Extractioncited by 170
  5. 2024LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Aheadcited by 155
  6. 2023Theory of Mind for Multi-Agent Collaboration via Large Language Modelscited by 135
  7. 2024LLM Multi-Agent Systems: Challenges and Open Problemscited by 120
  8. 2023GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extractioncited by 119
  9. 2023CodeIE: Large Code Generation Models are Better Few-Shot Information Extractorscited by 68
  10. 2023Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Modelscited by 52
  11. 2024Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Modelscited by 44
  12. 2023Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logiccited by 35
  13. 2024KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extractioncited by 34
  14. 2024IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimizationcited by 31
  15. 2024Instance-adaptive Zero-shot Chain-of-Thought Promptingcited by 26
  16. 2024Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Reviewcited by 6
  17. 2025Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reportscited by 3
  18. 2023Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic controlcited by 2

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