A language model passed a government licensing test in medicine! (USA)

FLAN-T5, a yummy model superior to GPT-3 | Narrativa



A language model passed a government licensing test in medicine! (U.S.)

Article: https://arxiv.org/pdf/2212.13138.pdf

Until now, only high school students benefited from automatic cheating on tests. There is already a mess with it!

By the way…
As part of the efforts to make technology accessible, *Google* trained a new model to cheat on the government medical exam in the USA.

The Med-PaLM model!
The Med-PaLM model (540 billion parameters) scored 67.6% on the MedQA USMLE (United States Medical Licensing Examination) compared to 60.2% Scored by Codex (175 billion parameters).
The passing scores for humans are 60.0%, And the expert score is 87.0%. The model based on Flan-PaLM has broken all records of results in many different tests and datasets related to medical issues.

Alignment training –
Similar to OpenAI, the Med-PaLM training was also attended by humans who went through the model’s answers in the second stage.
An interesting – innovation in the model is the running of the same question with several different instructions, for example:
“You help medical knowledge, provide useful, complete, and scientifically based answers to questions about Medicines”
“You help medical knowledge, provide useful, complete, and scientifically based answers to common questions about Medicine”
And so, step by step, we built the instruction that led to the best score.
It’s funny, but going over the state-of-the-art in the field involves sitting for hours on formulations.

About FLAN:
The FLAN model is the famous T5 text-to-text model trained with execute instructions (with many formulations per instruction).
The model is very-strong [Also, compared to GPT-3] and surprisingly understands human language!

About PaLM:
The PaLM model (500 billion parameters) was trained on 6144 TPUs (the most processing power for one model ever) on several “high-quality” (no longer known) datasets in English and various languages that include high-quality web documents, books, Wikipedia, conversations, and GitHub code. The authors used a tokenizer that contains all white spaces (especially, important for Code) and splits a Unicode character into bytes and numbers into individual tokens, one for each digit (so that PaLM can perform arithmetic calculations).


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