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Bhatnagar, Diksha (2023) Fine-Tuning Large Language Models for Domain-Specific Response Generation:A Case Study on Enhancing Peer Learning in Human Resource. Masters thesis, Dublin, National College of Ireland.
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Abstract
This research delves into front in-line Natural Language Processing (NLP) techniques, focused on devising innovative solutions specifically tailored for small-scale organisations to enhance precision and efficiency in the Human Resources domain. Keeping an employee-centric view in mind, the study molds Large Language Models (LLM) to excel in this domain.
The architecture focuses on generating contextually relevant answer prompts directed towards overall employee development, peer learning, and corporate culture. The strategy is underpinned by the utilisation of employee survey data on which the model is trained to glean insights from anonymised and consent-obtained responses. This synergy between natural language processing and survey data combines to fuel the system to offer accurate and contextually aware answers. Additionally, the dissertation explores the novel concepts of text synthesis, treating it as a self-contained entity. This intriguing avenue explores as well as promises potential applications in communication enhancements, though its inner workings are considered enigmatic, akin to a black box.
This study places a high priority on ethical concerns and compliance, ensuring the appropriate use of employee data and adherence to ethical research practices. The dissertation discusses the difficulties with ethics and compliance that were encountered during the study process and suggests solutions. It examines the significance of open data usage, clear consent, and the implementation of a finely tuned language model.
Integration of NLP with employee-centric data and venturing into the zone of multimedia synthesis, this study contributes to the rapidly growing field of AI-driven corporate solutions. The results demonstrate the effectiveness of specialised NLP methods in increasing communication dynamics and serve as a foundation for further study in this broad area.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Haque, Rejwanul UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management > Human Resource Management |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Tamara Malone |
| Date Deposited: | 08 Nov 2024 12:22 |
| Last Modified: | 08 Nov 2024 12:22 |
| URI: | https://norma.ncirl.ie/id/eprint/7171 |
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