@inproceedings{gavriilidis-etal-2024-enhancing,
title = "Enhancing Situation Awareness through Model-Based Explanation Generation",
author = "Gavriilidis, Konstantinos and
Konstas, Ioannis and
Hastie, Helen and
Pang, Wei",
editor = "Balloccu, Simone and
Kasner, Zden{\v{e}}k and
Pl{\'a}tek, Ond{\v{r}}ej and
Schmidtov{\'a}, Patr{\'\i}cia and
Onderkov{\'a}, Krist{\'y}na and
Lango, Mateusz and
Du{\v{s}}ek, Ond{\v{r}}ej and
Flek, Lucie and
Reiter, Ehud and
Gkatzia, Dimitra and
Mille, Simon",
booktitle = "Proceedings of the 2nd Workshop on Practical LLM-assisted Data-to-Text Generation",
month = sep,
year = "2024",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.practicald2t-1.2",
pages = "7--16",
abstract = "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. The code and dataset for this work are available at:{---}{---}{---}",
}
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%0 Conference Proceedings
%T Enhancing Situation Awareness through Model-Based Explanation Generation
%A Gavriilidis, Konstantinos
%A Konstas, Ioannis
%A Hastie, Helen
%A Pang, Wei
%Y Balloccu, Simone
%Y Kasner, Zdeněk
%Y Plátek, Ondřej
%Y Schmidtová, Patrícia
%Y Onderková, Kristýna
%Y Lango, Mateusz
%Y Dušek, Ondřej
%Y Flek, Lucie
%Y Reiter, Ehud
%Y Gkatzia, Dimitra
%Y Mille, Simon
%S Proceedings of the 2nd Workshop on Practical LLM-assisted Data-to-Text Generation
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F gavriilidis-etal-2024-enhancing
%X 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. The code and dataset for this work are available at:———
%U https://aclanthology.org/2024.practicald2t-1.2
%P 7-16
Markdown (Informal)
[Enhancing Situation Awareness through Model-Based Explanation Generation](https://aclanthology.org/2024.practicald2t-1.2) (Gavriilidis et al., PracticalD2T-WS 2024)
ACL