LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866918035478020096 |
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| author | Urlana, Ashok Kumar, Charaka Vinayak Singh, Ajeet Kumar Garlapati, Bala Mallikarjunarao Chalamala, Srinivasa Rao Mishra, Rahul |
| author_facet | Urlana, Ashok Kumar, Charaka Vinayak Singh, Ajeet Kumar Garlapati, Bala Mallikarjunarao Chalamala, Srinivasa Rao Mishra, Rahul |
| contents | Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks. From natural language processing and sentiment analysis to content generation and personalized recommendations, their unparalleled adaptability has facilitated widespread adoption across industries. This transformative shift driven by LLMs underscores the need to explore the underlying associated challenges and avenues for enhancement in their utilization. In this paper, our objective is to unravel and evaluate the obstacles and opportunities inherent in leveraging LLMs within an industrial context. To this end, we conduct a survey involving a group of industry practitioners, develop four research questions derived from the insights gathered, and examine 68 industry papers to address these questions and derive meaningful conclusions. We maintain the Github repository with the most recent papers in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14558 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey Urlana, Ashok Kumar, Charaka Vinayak Singh, Ajeet Kumar Garlapati, Bala Mallikarjunarao Chalamala, Srinivasa Rao Mishra, Rahul Computation and Language Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks. From natural language processing and sentiment analysis to content generation and personalized recommendations, their unparalleled adaptability has facilitated widespread adoption across industries. This transformative shift driven by LLMs underscores the need to explore the underlying associated challenges and avenues for enhancement in their utilization. In this paper, our objective is to unravel and evaluate the obstacles and opportunities inherent in leveraging LLMs within an industrial context. To this end, we conduct a survey involving a group of industry practitioners, develop four research questions derived from the insights gathered, and examine 68 industry papers to address these questions and derive meaningful conclusions. We maintain the Github repository with the most recent papers in the field. |
| title | LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.14558 |