SAMANTHA: A chatbot to assist users in training tasks to prevent workplace hazards
Authors: 
David Contreras Aguilar, Fernando Medina, Mauricio Oyanedel, Maria Salamó, Miquel Sànchez-MarrèAuthors Info & Claims
David Contreras Aguilar, Fernando Medina, Mauricio Oyanedel, Maria Salamó, Miquel Sànchez-MarrèAuthors Info & ClaimsArticle No.: 11, Pages 1 - 8
Abstract
In businesses, preventing workplace hazards becomes crucial. In order to limit negative effects on people, society, and the economy, it is crucial for both the organization and its employees to reduce accidents and occupational illnesses. Staff training programs are essential to a company’s preventative system. In this paper, we introduce SAMANTHA, an AI chatbot that helps reduce occupational dangers in the mining industry. Using pre-trained Large Language Models (LLMs), SAMANTHA assists users with training as well as daily work tasks, aiming to help employees in any circumstance to enhance well-being at work. Despite SAMANTHA’s concentration on the mining industry, its framework is sufficiently general to be readily applied to other industries. When SAMANTHA’s learning model is compared to the pre-trained ChatGPT3.5 model, it is clear that the suggested chatbot can accurately respond to users, and the evaluation conducted with real users indicates that they are satisfied with it.
References
[1]
Eleni Adamopoulou and Lefteris Moussiades. 2020. An Overview of Chatbot Technology. In Artificial Intelligence Applications and Innovations, Ilias Maglogiannis, Lazaros Iliadis, and Elias Pimenidis (Eds.). Springer International Publishing, Cham, 373–383.
[2]
Shadi AlZu’bi, Ala Mughaid, Fatima Quiam, and Samar Hendawi. 2023. Exploring the Capabilities and Limitations of ChatGPT and Alternative Big Language Models. (Apr. 2023). https://doi.org/10.47852/bonviewAIA3202820
[3]
Tita A. Bach, Jenny K. Kristiansen, Aleksandar Babic, and Alon Jacovi. 2023. Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Review. (2023). arxiv:2310.03392 [cs.HC]
[4]
D. Bowman, J. Gabbard, and D. Hix. 2002. A Survey of Usability Evaluation in Virtual Environments : Classification and Comparison of Methods. Presence: Teleoperators and Virtual Environments 11, 4 (2002), 404–424. https://doi.org/10.1162/105474602760204309
[5]
Petter Bae Brandtzaeg and Asbjørn Følstad. 2017. Why People Use Chatbots. In Internet Science, Ioannis Kompatsiaris, Jonathan Cave, Anna Satsiou, Georg Carle, Antonella Passani, Efstratios Kontopoulos, Sotiris Diplaris, and Donald McMillan (Eds.). Springer International Publishing, Cham, 377–392.
[6]
Davinia Rodríguez Cardona, Antje Henriette Annette Janssen, Nadine Guhr, Michael H. Breitner, and Julian Milde. 2021. A Matter of Trust? Examination of Chatbot Usage in Insurance Business. In Hawaii Int. Conference on System Sciences. https://api.semanticscholar.org/CorpusID:232414143
[7]
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, 2023. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. See https://vicuna. lmsys. org (accessed 14 April 2023) (2023).
[8]
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, 2023. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24, 240 (2023), 1–113.
[9]
Xinjie Deng and Zhonggen Yu. 2023. A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education. Sustainability 15 (02 2023), 2940. https://doi.org/10.3390/su15042940
[10]
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. Qlora: Efficient finetuning of quantized llms. arXiv preprint arXiv:2305.14314 (2023).
[11]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arxiv:1810.04805 [cs.CL]
[12]
Sören Dréano, Derek Molloy, and Noel Murphy. 2023. Embed_Llama: using LLM embeddings for the Metrics Shared Task. In Proceedings of the Eighth Conference on Machine Translation. 738–745.
[13]
Harry Barton Essel, Dimitrios Vlachopoulos, Akosua Tachie-Menson, Esi Eduafua Johnson, and Papa Kwame Baah. 2022. The impact of a virtual teaching assistant (chatbot) on students’ learning in Ghanaian higher education. International Journal of Educational Technology in Higher Education 19, 1 (2022), 57. https://doi.org/10.1186/s41239-022-00362-6
[14]
Luciano Floridi and Massimo Chiriatti. 2020. GPT-3: Its nature, scope, limits, and consequences. Minds and Machines 30 (2020), 681–694.
[15]
Muriel Figueredo Franco, Bruno Bastos Rodrigues, Eder John Scheid, Arthur Selle Jacobs, Christian Killer, Lisandro Zambenedetti Granville, and Burkhard Stiller. 2020. SecBot: a Business-Driven Conversational Agent for Cybersecurity Planning and Management. 2020 16th Int. Conference on Network and Service Management (CNSM) (2020), 1–7. https://api.semanticscholar.org/CorpusID:227279547
[16]
Zhenxu Guo, Qinge Wang, Chunyan Peng, Sunning Zhuang, and Biao Yang. 2024. Willingness to accept metaverse safety training for construction workers based on extended UTAUT. Frontiers in Public Health 11 (2024). https://doi.org/10.3389/fpubh.2023.1294203
[17]
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021).
[18]
Zhiqiang Hu, Yihuai Lan, Lei Wang, Wanyu Xu, Ee-Peng Lim, Roy Ka-Wei Lee, Lidong Bing, and Soujanya Poria. 2023. LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models. arXiv preprint arXiv:2304.01933 (2023).
[19]
Rahat Hussain, Aqsa Sabir, Do-Yeop Lee, Syed Farhan Alam Zaidi, Akeem Pedro, Muhammad Sibtain Abbas, and Chansik Park. 2024. Conversational AI-based VR system to improve construction safety training of migrant workers. Automation in Construction 160 (2024), 105315. https://doi.org/10.1016/j.autcon.2024.105315
[20]
Salma El Janati, Abdelilah Maach, and Driss El Ghanami. 2020. Adaptive e-Learning AI-Powered Chatbot based on Multimedia Indexing. International Journal of Advanced Computer Science and Applications 11, 12 (2020). https://doi.org/10.14569/IJACSA.2020.0111238
[21]
Bart P Knijnenburg, Martijn C Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction 22, 4-5 (2012), 441–504. https://doi.org/10.1007/s11257-011-9118-4
[22]
Lasha Labadze, Maya Grigolia, and Lela Machaidze. 2023. Role of AI chatbots in education: systematic literature review. Int. Journal of Educational Technology in Higher Education 20, 1 (2023), 56. https://doi.org/10.1186/s41239-023-00426-1
[23]
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arxiv:1907.11692 [cs.CL]
[24]
Soumi Majumder and Atreyee Mondal. 2021. Are chatbots really useful for human resource management?Int. Journal of Speech Technology (2021), 1–9.
[25]
Andrej Miklosik, Nina Evans, and Athar Qureshi. 2021. The Use of Chatbots in Digital Business Transformation: A Systematic Literature Review. IEEE Access 9 (07 2021), 106530–106539. https://doi.org/10.1109/ACCESS.2021.3100885
[26]
Mohammad Nuruzzaman and Omar Khadeer Hussain. 2020. IntelliBot: A Dialogue-based chatbot for the insurance industry. Knowledge-Based Systems 196 (2020), 105810.
[27]
Sinno Jialin Pan and Qiang Yang. 2009. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22, 10 (2009), 1345–1359.
[28]
Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay. 2023. The RefinedWeb dataset for Falcon LLM: outperforming curated corpora with web data, and web data only. arXiv preprint arXiv:2306.01116 (2023).
[29]
Juanan Pereira, María Fernández-Raga, Sara Osuna-Acedo, Margarita Roura-Redondo, Oskar Almazán-López, and Alejandro Buldón-Olalla. 2019. Promoting Learners’Voice Productions Using Chatbots as a Tool for Improving the Learning Process in a MOOC. Technology, Knowledge and Learning 24, 4 (2019), 545–565. https://doi.org/10.1007/s10758-019-09414-9
[30]
Pearl Pu, Li Chen, and Rong Hu. 2011. A User-centric Evaluation Framework for Recommender Systems. In Proceedings of the Fifth ACM Conference on Recommender Systems (Illinois, USA) (RecSys ’11). Association for Computing Machinery, NY, USA, 157–164. https://doi.org/10.1145/2043932.2043962
[31]
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang. 2020. Pre-trained models for natural language processing: A survey. Science China Technological Sciences 63, 10 (2020), 1872–1897.
[32]
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2023. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arxiv:1910.10683 [cs.LG]
[33]
Md. Saidur Rahaman, M. M. Tahmid Ahsan, Nishath Anjum, Md. Mizanur Rahman, and Md Nafizur Rahman. 2023. The AI Race is on! Google’s Bard and OpenAI’s ChatGPT Head to Head: An Opinion Article. https://ssrn.com/abstract=4351785 (2023). https://doi.org/10.2139/ssrn.4351785
[34]
Jürgen Rudolph, Shannon Tan, and Samson Tan. 2023. War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education. Journal of Applied Learning and Teaching 6, 1 (2023), 364–389. https://doi.org/10.37074/jalt.2023.6.1.23
[35]
Murray Shanahan. 2023. Talking About Large Language Models. arxiv:2212.03551 [cs.CL]
[36]
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford Alpaca: An Instruction-following LLaMA model. https://github.com/tatsu-lab/stanford_alpaca.
[37]
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023).
[38]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2023. Attention Is All You Need. arxiv:1706.03762 [cs.CL]
[39]
Haifeng Wang, Jiwei Li, Hua Wu, Eduard Hovy, and Yu Sun. 2022. Pre-trained language models and their applications. Engineering (2022).
[40]
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021. Finetuned language models are zero-shot learners. arXiv preprint arXiv:2109.01652 (2021).
[41]
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, 2023. A survey of large language models. arXiv preprint arXiv:2303.18223 (2023).
[42]
Xiaoe Zhu, Rita Yi Man Li, M. James C. Crabbe, and Khunanan Sukpascharoen. 2022. Can a chatbot enhance hazard awareness in the construction industry?Frontiers in Public Health 10 (2022). https://doi.org/10.3389/fpubh.2022.993700
Index Terms
- SAMANTHA: A chatbot to assist users in training tasks to prevent workplace hazards
Comments
Information & Contributors
Information
Published In
June 2024
155 pages
ISBN:9798400717871
DOI:10.1145/3657242
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 June 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
Conference
INTERACCION 2024
Acceptance Rates
Overall Acceptance Rate 109 of 163 submissions, 67%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 47Total Downloads
- Downloads (Last 12 months)47
- Downloads (Last 6 weeks)13
Reflects downloads up to 09 Nov 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format