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Although the development of artificial intelligence (AI) tools started in the 1950s,1 recent breakthroughs have placed powerful models in the hands of millions for vast potential uses. The advent of large language models (LLMs), such as Open AI’s ChatGPT2 and GPT-4,3 Google’s Bert4 and Bard (a fine-tuned LaMDA-2),5 among many others, has brought unprecedented potential, along with new ethical dilemmas. These models can produce coherent, and well-structured written content at a large scale, potentially assisting researchers and scientists in their work.
How do LLMs work?
LLMs, such as GPT-4 and Bard, are transformer architectures that are based on deep learning techniques. Briefly, they can generate text by predicting the next word in a sentence given all the previous words. Therefore, the choice of which word is used in which place within a sentence depends on probabilities governed by the massive amount of text data used to train them. Additionally, there is an element of randomness intertwined within the LLM algorithm to introduce a measure of inconsistency and simulate a perception of creativity. LLMs can capture long-range dependencies between words, meaning that they can ‘understand’ the context around a word within a sentence, leading to the perception of coherence and contextual relevance in the generated output. Without keeping how LLMs work in mind, it is easy to anthropomorphise it when interacting with it; one of several factors leading to the sensational uptake and hype in recent months.
Benefits of using LLMs for academic writing
The strengths of these computational models are many.6 Some of the most obvious advantages of LLMs are increasing writing efficiency and generating coherent texts. They can do so at varying reading levels and for a variety of purposes, as directed by the users’ prompts. If …
Contributors FA concevied the idea, drafted the manuscript and approved the final version.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.