Spor ve Teknoloji: Etkileşim ve Dönüşümün Yolculuğu

OpenAi GPT 3.5,

doi:10.58830/ozgur.pub222.c951

Cited by 77 publications

(109 citation statements)

References 20 publications

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“…LLMs are deep learning frameworks designed to process natural language text (Table 1 and Table 2 for a glossary of technical terms). 5,6 Unlike more traditional machine learning models, such as naïve Bayes classifiers, which rely on explicit labels ("happy" or "sad"), features (for example, full words or phrases), and rules to identify patterns, LLMs learn to recognize patterns and fill gaps or generate text using deep learning with vast amounts of data. LLMs are typically trained using large text corpora, such as text on the Internet, Wikipedia, books, newspaper articles, and other documents.…”

Section: Introductionmentioning

confidence: 99%

Large Language Models in Neurology Research and Future Practice

Romano,

Shih,

Paschalidis

et al. 2023

Neurology

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Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records and gain insights on specific topics in neurology. Additionally, we explore use cases for LLMs such as early diagnosis, supporting patient and caregivers, and acting as an assistant for clinicians. We point to the potential ethical and technical challenges raised by LLMs, such as concerns about privacy and data security, potential biases in the data for model training, and the need for careful validation of results. Researchers must consider these challenges and take steps to address them to ensure that their work is conducted in a safe and responsible manner. Despite these challenges, LLMs offer promising opportunities for improving care and treatment of various neurological disorders.

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Section: Introductionmentioning

confidence: 99%

Large Language Models in Neurology Research and Future Practice

Romano,

Shih,

Paschalidis

et al. 2023

Neurology

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“…Large language models (LLMs) such as GPT have demonstrated powerful capabilities in natural language processing tasks including question answering, summarization, and text generation ( [1], [2], [3], [4], [5], [6]). However, applying LLMs to other domains like biology remains an open challenge.…”

Section: Introductionmentioning

confidence: 99%

Cell2Sentence: Teaching Large Language Models the Language of Biology

Levine,

Lévy,

Rizvi

et al. 2023

Preprint

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Large language models like GPT have shown impressive performance on natural language tasks. Here, we present a novel method to directly adapt these pretrained models to a biological context, specifically single-cell transcriptomics, by representing gene expression data as text. Our Cell2Sentence approach converts each cell's gene expression profile into a sequence of gene names ordered by expression level. We show that these gene sequences, which we term "cell sentences", can be used to fine-tune causal language models like GPT-2. Critically, we find that natural language pretraining boosts model performance on cell sentence tasks. When fine-tuned on cell sentences, GPT-2 generates biologically valid cells when prompted with a cell type. Conversely, it can also accurately predict cell type labels when prompted with cell sentences. This demonstrates that language models fine-tuned using Cell2Sentence can gain a biological understanding of single-cell data, while retaining their ability to generate text. Our approach provides a simple, adaptable framework to combine natural language and transcriptomics using existing models and libraries. Our code is available at: https://github.com/vandijklab/cell2sentence-ft.

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“…5 Recent iterations have excelled in knowledge benchmarks such as the Uniform Bar Examination and even produced academic writing virtually indistinguishable from humanauthored work. 6,7 It is yet to be determined if ChatGPT could aid in crafting LORs, particularly in high-stakes contexts like faculty promotion. To determine the feasibility of this process and whether there is a significant difference between AI-and human-authored letters, we conducted a study aimed at determining whether academic physicians can distinguish between the two.…”

Section: Introductionmentioning

confidence: 99%

Brain versus bot: Distinguishing letters of recommendation authored by humans compared with artificial intelligence

Preiksaitis,

Nash,

Gottlieb

et al. 2023

AEM Education and Training

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ObjectivesLetters of recommendation (LORs) are essential within academic medicine, affecting a number of important decisions regarding advancement, yet these letters take significant amounts of time and labor to prepare. The use of generative artificial intelligence (AI) tools, such as ChatGPT, are gaining popularity for a variety of academic writing tasks and offer an innovative solution to relieve the burden of letter writing. It is yet to be determined if ChatGPT could aid in crafting LORs, particularly in high‐stakes contexts like faculty promotion. To determine the feasibility of this process and whether there is a significant difference between AI and human‐authored letters, we conducted a study aimed at determining whether academic physicians can distinguish between the two.MethodsA quasi‐experimental study was conducted using a single‐blind design. Academic physicians with experience in reviewing LORs were presented with LORs for promotion to associate professor, written by either humans or AI. Participants reviewed LORs and identified the authorship. Statistical analysis was performed to determine accuracy in distinguishing between human and AI‐authored LORs. Additionally, the perceived quality and persuasiveness of the LORs were compared based on suspected and actual authorship.ResultsA total of 32 participants completed letter review. The mean accuracy of distinguishing between human‐ versus AI‐authored LORs was 59.4%. The reviewer's certainty and time spent deliberating did not significantly impact accuracy. LORs suspected to be human‐authored were rated more favorably in terms of quality and persuasiveness. A difference in gender‐biased language was observed in our letters: human‐authored letters contained significantly more female‐associated words, while the majority of AI‐authored letters tended to use more male‐associated words.ConclusionsParticipants were unable to reliably differentiate between human‐ and AI‐authored LORs for promotion. AI may be able to generate LORs and relieve the burden of letter writing for academicians. New strategies, policies, and guidelines are needed to balance the benefits of AI while preserving integrity and fairness in academic promotion decisions.

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Spor ve Teknoloji: Etkileşim ve Dönüşümün Yolculuğu

Cited by 77 publications

References 20 publications

Large Language Models in Neurology Research and Future Practice

Large Language Models in Neurology Research and Future Practice

Cell2Sentence: Teaching Large Language Models the Language of Biology

Brain versus bot: Distinguishing letters of recommendation authored by humans compared with artificial intelligence

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