@inproceedings{patil-etal-2024-improving,
title = "Improving Industrial Safety by Auto-Generating Case-specific Preventive Recommendations",
author = "Patil, Sangameshwar and
Koundanya, Sumit and
Kumbhar, Shubham and
Kumar, Alok",
editor = "Dementieva, Daryna and
Ignat, Oana and
Jin, Zhijing and
Mihalcea, Rada and
Piatti, Giorgio and
Tetreault, Joel and
Wilson, Steven and
Zhao, Jieyu",
booktitle = "Proceedings of the Third Workshop on NLP for Positive Impact",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.nlp4pi-1.30",
pages = "349--353",
abstract = "In this paper, we propose a novel application to improve industrial safety by generating preventive recommendations using LLMs. Using a dataset of 275 incidents representing 11 different incident types sampled from real-life OSHA incidents, we compare three different LLMs to evaluate the quality of preventive recommendations generated by them. We also show that LLMs are not a panacea for the preventive recommendation generation task. They have limitations and can produce responses that are incorrect or irrelevant. We found that about 65{\%} of the output from Vicuna model was not acceptable at all at the basic readability and other sanity checks level. Mistral and Phi{\_}3 are better than Vicuna, but not all of their recommendations are of similar quality. We find that for a given safety incident case, the generated recommendations can be categorized as specific, generic, or irrelevant. This helps us to better quantify and compare the performance of the models. This paper is among the initial and novel work for the preventive recommendation generation problem. We believe it will pave way for use of NLP to positively impact the industrial safety.",
}
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%0 Conference Proceedings
%T Improving Industrial Safety by Auto-Generating Case-specific Preventive Recommendations
%A Patil, Sangameshwar
%A Koundanya, Sumit
%A Kumbhar, Shubham
%A Kumar, Alok
%Y Dementieva, Daryna
%Y Ignat, Oana
%Y Jin, Zhijing
%Y Mihalcea, Rada
%Y Piatti, Giorgio
%Y Tetreault, Joel
%Y Wilson, Steven
%Y Zhao, Jieyu
%S Proceedings of the Third Workshop on NLP for Positive Impact
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F patil-etal-2024-improving
%X In this paper, we propose a novel application to improve industrial safety by generating preventive recommendations using LLMs. Using a dataset of 275 incidents representing 11 different incident types sampled from real-life OSHA incidents, we compare three different LLMs to evaluate the quality of preventive recommendations generated by them. We also show that LLMs are not a panacea for the preventive recommendation generation task. They have limitations and can produce responses that are incorrect or irrelevant. We found that about 65% of the output from Vicuna model was not acceptable at all at the basic readability and other sanity checks level. Mistral and Phi_3 are better than Vicuna, but not all of their recommendations are of similar quality. We find that for a given safety incident case, the generated recommendations can be categorized as specific, generic, or irrelevant. This helps us to better quantify and compare the performance of the models. This paper is among the initial and novel work for the preventive recommendation generation problem. We believe it will pave way for use of NLP to positively impact the industrial safety.
%U https://aclanthology.org/2024.nlp4pi-1.30
%P 349-353
Markdown (Informal)
[Improving Industrial Safety by Auto-Generating Case-specific Preventive Recommendations](https://aclanthology.org/2024.nlp4pi-1.30) (Patil et al., NLP4PI 2024)
ACL