The impending impacts of large language models on medical education
Informations
- Type:
- article
- Auteurs:
- Ahn Sangzin
- Pertinence:
-
Faible
- Référence:
- Doi:
- 10.3946/kjme.2023.253
- Mots-clés:
- Url:
- https://www.kjme.kr/journal/view.php?doi=10.3946/kjme.2023.253
- Date de publication:
- 02/2023
- Résumé:
- Discution sur les utilisations possibles des llms dans les formations médicales. Utilisation de patients chat bots...
- Abstract:
-
The advent of large language models
Large language models (LLMs) are machine learning models that are trained on extremely large datasets of text and are capable of multiple natural language processing tasks, such as translation, summarization, and grammar correction. By learning which word (or token) is most probable to appear after a sequence of preceding words in a self-supervised (no labeling required) manner, the LLM is able to predict the next single word, and is therefore described as generative. A prompt, a chunk of text that usually describes the objective, is provided to the model, and by iteratively predicting the next word to follow the prompt, the LLM can generate a long sequence of coherent and grammatically correct text. At first glance, it may seem that LLMs can only perform an autocomplete function, but by carefully crafting the prompt (also known as prompt engineering), LLMs can perform a variety of tasks. For example, if the LLM is prompted with “Translate this into French: what rooms do you have available?” the model will respond “Quels sont les chambres que vous avez disponibles?” Additionally, if the prompt “Correct this to standard English: she no went to the market.” is provided, the response will be “She did not go to the market [1].”
It is well known that scaling up language models (amount of computation, number of model parameters, and training dataset size) results in better performance in downstream tasks. Often, the effect of scaling increases predictably, but some emergent abilities are observed not in smaller models but in larger models. Some examples include arithmetic, transliterating from the international phonetic alphabet, recovering a word from its scrambled letters, and question answering [2]. The advancement in prompt engineering and deeper studies in the scaling law of language models are leading to an era where LLMs are becoming increasingly versatile in a variety of tasks, which were not considered possible a few years ago. In this article, the author will discuss four abilities of LLMs and their potential impacts on medical education. - Pdf:
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Références
1 articles
| Titre | Type | Pertinence | Auteurs | Date Publication | Références | Citations | Actions |
|---|---|---|---|---|---|---|---|
| AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompt... | inproceedings | Haute | Wu, Tongshuang and Terry, Michael and Cai, Carrie Jun | 04/2022 | 0 | 3 |
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| Nom | Nombre d'articles | Actions |
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| Ahn Sangzin | 1 |