As of December 13, 2022, ChatGPT, the new language processing AI from OpenAI, is making waves in the tech industry. The advanced model, which is trained to generate human-like text, is already being hailed as a game-changer for businesses that rely on natural language processing.
ChatGPT’s ability to understand and respond to a wide range of topics has been particularly impressive, with some even suggesting that it has the potential to revolutionize the way we interact with technology. Many experts believe that ChatGPT’s advanced capabilities will be a valuable asset for companies in fields such as customer service, online education, and market research.
One of the key advantages of ChatGPT is its ability to learn and adapt quickly to new information. This means that it can be trained to handle new topics and tasks without the need for extensive retraining. Additionally, ChatGPT is highly scalable, which makes it well-suited for use in large-scale applications.
So far, the response to ChatGPT has been overwhelmingly positive, with many praising its advanced capabilities and ease of use. It remains to be seen how ChatGPT will be used in the coming years, but it’s clear that it has the potential to be a major player in the world of natural language processing.
While #chatgpt has been hogging the limelight as of late on all matters associated with AI and LLM (large language models), the research teams at Google and DeepMind quietly published a paper last week outlining their impressive work in developing an open source LLM tool called Med-PaLM. Unlike ChatGPT, which is trained on an extraordinarily massive but broad range of datasets for the purpose of serving as a general natural language tool, #MedPaLM was designed to respond to medical questions, either from medical professionals or consumers (i.e. patients).
To do this, the team created a new dataset of medical questions (to serve as a benchmark) by combining 6 existing datasets. They then evaluated the performance of the model by analyzing its responses to questions based on factuality, precision, possible harm and bias.
What did they find?
While the MedPaLM model performance was impressive and certainly superior to other NLP models investigated to date, it was still inferior to clinicians, particularly in incorrect retrieval of information (16.9% for MedPaLM vs 3.6% for human clinicians), evidence of incorrect reasoning (10.1% vs 2.1%) and inappropriate/incorrect content of responses (18.7% vs. 1.4%).
Bottomline, MedPalM is a huge step forward both in moving the needle towards a viable LLM that can be used for clinical knowledge, as well as in establishing frameworks to evaluate such models.
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