Abstract / Description of output
Robots are often deployed in remote locations for tasks such as exploration, where users cannot directly perceive the agent and its environment. For Human-In-The-Loop applications, operators must have a comprehensive understanding of the robot’s current state and its environment to take necessary actions and effectively assist the agent. In this work, we compare different explanation styles to determine the most effective way to convey real-time updates to users. Additionally, we formulate these explanation styles as separate fine-tuning tasks and assess the effectiveness of large language models in delivering in-mission updates to maintain situation awareness.
Original language | English |
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Title of host publication | Proceedings of the 2nd Workshop on Practical LLM-assisted Data-to-Text Generation |
Editors | Simone Balloccu, Zdeněk Kasner, Ondřej Plátek, Patrícia Schmidtová, Kristýna Onderková, Mateusz Lango, Ondřej Dušek, Lucie Flek, Ehud Reiter, Dimitra Gkatzia, Simon Mille |
Publisher | Association for Computational Linguistics |
Pages | 7-16 |
Number of pages | 10 |
ISBN (Electronic) | 9798891761261 |
Publication status | Published - 24 Sept 2024 |
Event | The 2nd Workshop on Practical LLM-assisted Data-to-Text Generation - Tokyo, Japan Duration: 23 Sept 2024 → 23 Sept 2024 https://practicald2t.github.io/ |
Workshop
Workshop | The 2nd Workshop on Practical LLM-assisted Data-to-Text Generation |
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Abbreviated title | Practical D2T 2024 |
Country/Territory | Japan |
City | Tokyo |
Period | 23/09/24 → 23/09/24 |
Internet address |