An analysis of large language models: their impact and potential applications

Bharathi Mohan, G.; Prasanna Kumar, R.; Vishal Krishh, P.; Keerthinathan, A.; Lavanya, G.; Meghana, Meka Kavya Uma; Sulthana, Sheba; Doss, Srinath

doi:10.1007/s10115-024-02120-8

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…Recently, the most important artifacts built upon the transformer architecture are undoubtedly large language models (LLMs). LLMs such as GPT, Llama, Claude, and Falcon have significantly impacted various fields, including technology, education, creativity, business, healthcare and the society at large . The revolutional power of LLMs comes from their ability to efficiently process enormous text corpus, capture the underlying associations/patterns, and reproduce the information via an intuitive interface with human-like communication.…”

Section: Future Directionsmentioning

confidence: 99%

“…LLMs such as GPT, Llama, Claude, and Falcon have significantly impacted various fields, including technology, education, creativity, business, healthcare and the society at large. 189 The revolutional power of LLMs comes from their ability to efficiently process enormous text corpus, capture the underlying associations/patterns, and reproduce the information via an intuitive interface with human-like communication. Driven by this wave of success, there is an rising interest in applying LLMs to assist scientific research.…”

Section: Future Directionsmentioning

confidence: 99%

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Application of Transformers in Cheminformatics

Luong,

Singh

2024

J. Chem. Inf. Model.

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By accelerating time-consuming processes with high efficiency, computing has become an essential part of many modern chemical pipelines. Machine learning is a class of computing methods that can discover patterns within chemical data and utilize this knowledge for a wide variety of downstream tasks, such as property prediction or substance generation. The complex and diverse chemical space requires complex machine learning architectures with great learning power. Recently, learning models based on transformer architectures have revolutionized multiple domains of machine learning, including natural language processing and computer vision. Naturally, there have been ongoing endeavors in adopting these techniques to the chemical domain, resulting in a surge of publications within a short period. The diversity of chemical structures, use cases, and learning models necessitate a comprehensive summarization of existing works. In this paper, we review recent innovations in adapting transformers to solve learning problems in chemistry. Because chemical data is diverse and complex, we structure our discussion based on chemical representations. Specifically, we highlight the strengths and weaknesses of each representation, the current progress of adapting transformer architectures, and future directions.

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Section: Future Directionsmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

Application of Transformers in Cheminformatics

Luong,

Singh

2024

J. Chem. Inf. Model.

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Uncertainty quantification in large language models through convex hull analysis

Catak,

Kuzlu

2024

Discov Artif Intell

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Examining the responsible use of zero-shot AI approaches to scoring essays

Johnson,

Zhang

2024

Sci Rep

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The promise of AI to alleviate the burdens of grading and potentially enhance writing instruction is an exciting prospect. However, we believe it is crucial to emphasize that the accuracy of AI is only one component of its responsible use in education. Various governmental agencies, such as NIST in the US, and non-governmental agencies like the UN, UNESCO, and OECD have published guidance on the responsible use of AI, which we have synthesized to come up with our principles for the responsible use of AI in assessments at ETS. Our principles include fairness and bias mitigation; privacy & security; transparency, explainability, and accountability; educational impact & integrity; and continuous improvement. The accuracy of AI-scoring is one component of our principles related to educational impact & integrity. In this work, we share our thoughts on fairness & bias mitigation, and transparency & explainability. We demonstrate an empirical evaluation of zero-shot scoring using GTP-4o, with an emphasis on fairness evaluations and explainability of these automated scoring models.

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An analysis of large language models: their impact and potential applications

Cited by 6 publications

References 34 publications

Application of Transformers in Cheminformatics

Application of Transformers in Cheminformatics

Uncertainty quantification in large language models through convex hull analysis

Examining the responsible use of zero-shot AI approaches to scoring essays

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