


Microsoft's latest research once again proves the power of Prompt Project -
Without additional fine-tuning or expert planning, GPT-4 can become an "expert" with just prompts.
Using the latest prompting strategy they proposed Medprompt, in the medical professional field, GPT-4 achieved the best results in the nine test sets of MultiMed QA.
On the MedQA data set (United States Medical Licensing Examination questions), Medprompt made GPT-4's accuracy exceed 90% for the first time, surpassed BioGPT and Med-PaLM Waiting for a number of fine-tuning methods.
The researchers also stated that the Medprompt method is universal and is not only applicable to medicine, but can also be extended to electrical engineering, machine learning, law and other majors.
As soon as this study was shared on X (formerly Twitter), it attracted the attention of many netizens.
Wharton School professor Ethan Mollick, Artificial Intuition author Carlos E. Perez, etc. have all forwarded and shared it.
Carlos E. Perez said that "an excellent prompting strategy can take a lot of fine-tuning":
Some netizens said that they have had this premonition for a long time. , it’s really cool to see the results coming out now!
Some netizens think this is really "radical"
GPT-4 is a technology that can change the industry, but we are still far away The limits of the prompts have not been hit, nor have the limits of fine tuning been reached.
Combined prompt strategies, "transform" into an expert
Medprompt is a combination of multiple prompt strategies, including three magic weapons:
- Dynamic few-shot selection
- Self-generated chain of thought
- Choice shuffling ensemble )
Next, we will introduce them one by one
Dynamic few-sample selection
Few-sample learning is to make the model fast An effective way to learn context. Simply put, input some examples, let the model quickly adapt to a specific domain, and learn to follow the format of the task.
This kind of few-sample examples used for specific task prompts are usually fixed, so there are high requirements for the representativeness and breadth of the examples.
The previous method was to let domain expertsmanually produce examples, but even so, there is no guarantee that the fixed few-sample examples curated by experts are representative in each task.
Microsoft researchers proposed a method of dynamic few-shot examples, so
The idea is that the task training set can be used as a source of few-shot examples, and if the training set is large enough, then it can Select different few-shot examples for different task inputs.
In terms of specific operations, the researchers first used the text-embedding-ada-002 model to generate vector representations for each training sample and test sample. Then, for each test sample, by comparing the similarity of the vectors, the k most similar samples are selected from the training samples
Compared with the fine-tuning method, dynamic few-shot selection makes use of the training data, But it doesn't require extensive updates to model parameters.
Self-generated chain of thinking
The chain of thinking (CoT) method is a method that lets the model think step by step and generate a series of intermediate reasoning steps
Previous methods relied on experts Manually write some examples with prompt thought chains
Here, the researchers found that GPT-4 can be simply asked to generate thought chains for training examples using the following prompt:
But the researchers also pointed out that this automatically generated thinking chain may contain wrong reasoning steps, so they set up a verification tag as a filter, which can effectively reduce errors.
Compared with the thinking chain examples hand-crafted by experts in the Med-PaLM 2 model, the basic principles of the thinking chain generated by GPT-4 are longer, and the step-by-step reasoning logic is more fine-grained.
Option Shuffling Integration
GPT-4 may have a bias when dealing with multiple choice questions, that is, it tends to always choose A or always choose B, no matter what the content of the option is. , this is the position deviation
In order to solve this problem, the researchers decided to rearrange the order of the original options to reduce the impact. For example, the original order of options is ABCD, which can be changed to BCDA, CDAB, etc.
Then let GPT-4 do multiple rounds of predictions, using a different order of options in each round. This "forces" GPT-4 to consider the content of the options.
Finally, vote on the results of multiple rounds of predictions and choose the most consistent and correct option.
The combination of the above prompt strategies is Medprompt. Let’s take a look at the test results.
Multiple Test Optimal
In the test, the researchers used the MultiMed QA evaluation benchmark.
GPT-4, which uses the Medprompt prompting strategy, achieved the highest scores in all nine benchmark data sets of MultiMedQA, better than Flan-PaLM 540B and Med-PaLM 2.
In addition, the researchers also discussed the performance of the Medprompt strategy on "Eyes-Off" data. The so-called "Eyes-Off" data refers to data that the model has never seen during the training or optimization process. It is used to test whether the model is overfitting the training data
Results GPT-4 combined with the Medprompt strategy performed well on multiple medical benchmark data sets, with an average accuracy of 91.3%.
The researchers conducted ablation experiments on the MedQA dataset to explore the relative contributions of the three components to the overall performance
In which thought chains are automatically generated The steps play the biggest role in improving performance
The score of the thinking chain automatically generated by GPT-4 is higher than the score planned by experts in Med-PaLM 2, and does not require Manual intervention
#Finally, the researchers also explored Medprompt’s cross-domain generalization capabilities, using six different datasets from the MMLU benchmark, covering electrical engineering , machine learning, philosophy, professional accounting, professional law and professional psychology issues.
Two additional datasets containing NCLEX (National Nurse Licensing Examination) questions have also been added.
The results show that the effect of Medprompt on these data sets is similar to the improvement on the MultiMedQA medical data set, with the average accuracy increased by 7.3%.
Please click the following link to view the paper: https://arxiv.org/pdf/2311.16452.pdf
The above is the detailed content of Microsoft turned GPT-4 into a medical expert with just the 'Prompt Project'! More than a dozen highly fine-tuned models, the professional test accuracy exceeded 90% for the first time. For more information, please follow other related articles on the PHP Chinese website!

1 前言在发布DALL·E的15个月后,OpenAI在今年春天带了续作DALL·E 2,以其更加惊艳的效果和丰富的可玩性迅速占领了各大AI社区的头条。近年来,随着生成对抗网络(GAN)、变分自编码器(VAE)、扩散模型(Diffusion models)的出现,深度学习已向世人展现其强大的图像生成能力;加上GPT-3、BERT等NLP模型的成功,人类正逐步打破文本和图像的信息界限。在DALL·E 2中,只需输入简单的文本(prompt),它就可以生成多张1024*1024的高清图像。这些图像甚至

“Making large models smaller”这是很多语言模型研究人员的学术追求,针对大模型昂贵的环境和训练成本,陈丹琦在智源大会青源学术年会上做了题为“Making large models smaller”的特邀报告。报告中重点提及了基于记忆增强的TRIME算法和基于粗细粒度联合剪枝和逐层蒸馏的CofiPruning算法。前者能够在不改变模型结构的基础上兼顾语言模型困惑度和检索速度方面的优势;而后者可以在保证下游任务准确度的同时实现更快的处理速度,具有更小的模型结构。陈丹琦 普

Wav2vec 2.0 [1],HuBERT [2] 和 WavLM [3] 等语音预训练模型,通过在多达上万小时的无标注语音数据(如 Libri-light )上的自监督学习,显著提升了自动语音识别(Automatic Speech Recognition, ASR),语音合成(Text-to-speech, TTS)和语音转换(Voice Conversation,VC)等语音下游任务的性能。然而这些模型都没有公开的中文版本,不便于应用在中文语音研究场景。 WenetSpeech [4] 是

由于复杂的注意力机制和模型设计,大多数现有的视觉 Transformer(ViT)在现实的工业部署场景中不能像卷积神经网络(CNN)那样高效地执行。这就带来了一个问题:视觉神经网络能否像 CNN 一样快速推断并像 ViT 一样强大?近期一些工作试图设计 CNN-Transformer 混合架构来解决这个问题,但这些工作的整体性能远不能令人满意。基于此,来自字节跳动的研究者提出了一种能在现实工业场景中有效部署的下一代视觉 Transformer——Next-ViT。从延迟 / 准确性权衡的角度看,

3月27号,Stability AI的创始人兼首席执行官Emad Mostaque在一条推文中宣布,Stable Diffusion XL 现已可用于公开测试。以下是一些事项:“XL”不是这个新的AI模型的官方名称。一旦发布稳定性AI公司的官方公告,名称将会更改。与先前版本相比,图像质量有所提高与先前版本相比,图像生成速度大大加快。示例图像让我们看看新旧AI模型在结果上的差异。Prompt: Luxury sports car with aerodynamic curves, shot in a

人工智能就是一个「拼财力」的行业,如果没有高性能计算设备,别说开发基础模型,就连微调模型都做不到。但如果只靠拼硬件,单靠当前计算性能的发展速度,迟早有一天无法满足日益膨胀的需求,所以还需要配套的软件来协调统筹计算能力,这时候就需要用到「智能计算」技术。最近,来自之江实验室、中国工程院、国防科技大学、浙江大学等多达十二个国内外研究机构共同发表了一篇论文,首次对智能计算领域进行了全面的调研,涵盖了理论基础、智能与计算的技术融合、重要应用、挑战和未来前景。论文链接:https://spj.scien

译者 | 李睿审校 | 孙淑娟近年来, Transformer 机器学习模型已经成为深度学习和深度神经网络技术进步的主要亮点之一。它主要用于自然语言处理中的高级应用。谷歌正在使用它来增强其搜索引擎结果。OpenAI 使用 Transformer 创建了著名的 GPT-2和 GPT-3模型。自从2017年首次亮相以来,Transformer 架构不断发展并扩展到多种不同的变体,从语言任务扩展到其他领域。它们已被用于时间序列预测。它们是 DeepMind 的蛋白质结构预测模型 AlphaFold

说起2010年南非世界杯的最大网红,一定非「章鱼保罗」莫属!这只位于德国海洋生物中心的神奇章鱼,不仅成功预测了德国队全部七场比赛的结果,还顺利地选出了最终的总冠军西班牙队。不幸的是,保罗已经永远地离开了我们,但它的「遗产」却在人们预测足球比赛结果的尝试中持续存在。在艾伦图灵研究所(The Alan Turing Institute),随着2022年卡塔尔世界杯的持续进行,三位研究员Nick Barlow、Jack Roberts和Ryan Chan决定用一种AI算法预测今年的冠军归属。预测模型图


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

SublimeText3 Chinese version
Chinese version, very easy to use

Dreamweaver CS6
Visual web development tools

mPDF
mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

DVWA
Damn Vulnerable Web App (DVWA) is a PHP/MySQL web application that is very vulnerable. Its main goals are to be an aid for security professionals to test their skills and tools in a legal environment, to help web developers better understand the process of securing web applications, and to help teachers/students teach/learn in a classroom environment Web application security. The goal of DVWA is to practice some of the most common web vulnerabilities through a simple and straightforward interface, with varying degrees of difficulty. Please note that this software
