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This thesis investigates the computational modeling of belief and related cognitive states as expressed in text and speech. Understanding how speakers or authors convey commitment, certainty, and emotions is crucial for language understanding, yet poses significant challenges for current NLP systems. We present a comprehensive study spanning multiple facets of belief prediction. We begin by re-examining the widely used FactBank corpus, correcting a critical projection error and establishing new state-of-the-art results for author-only belief prediction through multi-task learning and error analysis. We then tackle the more complex task of source-and-target belief prediction, introducing a novel generative framework using Flan-T5. This includes developing a structured database representation for FactBank and proposing a linearized tree generation approach, culminating in the BeLeaf system for visualization and analysis, which achieves state-of-the-art performance on both FactBank and the MDP corpus. With the rise of large language models (LLMs), we investigate their zero-shot capabilities for the source-and-target belief task. We propose Unified and Hybrid prompting frameworks, finding that while current LLMs struggle, particularly with nested beliefs, our Hybrid approach paired with reasoning-focused LLMs achieves new state-of-the-art results on FactBank. Finally, we explore the role of multimodality among multiple cognitive states. We present the first study on multimodal belief prediction using the CB-Prosody corpus, demonstrating that integrating audio features via fine-tuned Whisper models significantly improves performance over text-only BERT models. We further introduce Synthetic Audio Data (SAD), showing that even synthetic audio generated by TTS systems provides orthogonal, beneficial signals for various cognitive state tasks (belief, emotion, sentiment). We conclude by presenting OmniVox, the first systematic evaluation of omni-LLMs for zero-shot emotion recognition directly from audio, demonstrating their competitiveness with fine-tuned models and analyzing their acoustic reasoning capabilities.
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This content will become publicly available on November 1, 2025
LEVIOSA: Natural Language-Based Uncrewed Aerial Vehicle Trajectory Generation
This paper presents LEVIOSA, a novel framework for text- and speech-based uncrewed aerial vehicle (UAV) trajectory generation. By leveraging multimodal large language models (LLMs) to interpret natural language commands, the system converts text and audio inputs into executable flight paths for UAV swarms. The approach aims to simplify the complex task of multi-UAV trajectory generation, which has significant applications in fields such as search and rescue, agriculture, infrastructure inspection, and entertainment. The framework involves two key innovations: a multi-critic consensus mechanism to evaluate trajectory quality and a hierarchical prompt structuring for improved task execution. The innovations ensure fidelity to user goals. The framework integrates several multimodal LLMs for high-level planning, converting natural language inputs into 3D waypoints that guide UAV movements and per-UAV low-level controllers to control each UAV in executing its assigned 3D waypoint path based on the high-level plan. The methodology was tested on various trajectory types with promising accuracy, synchronization, and collision avoidance results. The findings pave the way for more intuitive human–robot interactions and advanced multi-UAV coordination.
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- Award ID(s):
- 2138206
- PAR ID:
- 10637601
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Electronics
- Volume:
- 13
- Issue:
- 22
- ISSN:
- 2079-9292
- Page Range / eLocation ID:
- 4508
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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