15,325
Large Language Models
2023-03-14
GPT-4
34
1 Extracted insights from varied texts.
Overview
Large Language Models#21 most recent
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Generated by ChatGPT
GPT-4 is a deep learning model developed by OpenAI that is capable of accepting both image and text inputs and emitting text outputs. It has achieved human-level performance on various professional and academic benchmarks, although it is less capable than humans in many real-world scenarios.
GPT-4 is a large multimodal model that has been trained on a vast corpus of data in order to learn to generate coherent and contextually appropriate text in response to various inputs.
It has been designed to be more reliable, creative, and able to handle much more nuanced instructions than its predecessor, GPT-3.5. GPT-4's capabilities have been tested on a variety of benchmarks, including simulating exams that were originally designed for humans.
GPT-4 has also been evaluated on traditional benchmarks designed for machine learning models, where it considerably outperforms existing large language models alongside most state-of-the-art models.
GPT-4's text input capability is currently available via ChatGPT and the API, with the image input capability being prepared for wider availability through collaboration with a single partner.
OpenAI has also open-sourced OpenAI Evals, a framework for automated evaluation of AI model performance, to allow anyone to report shortcomings in their models to help guide further improvements.
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Comments(1)
علی اکبر خانبابائی
Dec 31, 2023
چطور در متود زیر بعد از جواب دادن ajax مقداری را برگردانم از متود یعنی اینکه متود صبر کند تا مقداری از ajax برگردد سپس به راه خود ادامه دهد
تمام این ها در jquery است
function ajaxBehpardakht(rawPrice/*, tarefelist, etebar, data, totalTarefe*/) {
var myJSON1 = '{"ServiceCode" :"1","Amount":"' rawPrice '","PayerId":"","PcID":""}';
var multiaccount = localStorage.getItem('multiaccount');
/* if (multiaccount == "true") {
var BofeId = localStorage.getItem('BofeId');
var KhedmatId = localStorage.getItem('KhedmatId');
var myJSON1 = {
"ServiceCode": "1", "TotalAmount": rawPrice "", "RequestList": [
{ "AccountID": BofeId "", "Amount": etebar "", "PayerID": "0" },
{ "AccountID": KhedmatId "", "Amount": totalTarefe "", "PayerID": "0" },
], "PrintDetail": "1", "PcID": "1234"
}
}
*/
var myObj1 = JSON.parse(myJSON1);
$.ajax({
type: "POST",
contentType: "application/json",
//<!-- Access-Control-Allow:*, -->
url: "http://localhost:1024/bpmpospc/service",
data: myJSON1,
crossDomain: true,
success: function (obj) {
console.log(':: ' obj);
var x = (obj.ReturnCode);
console.log(":: Result.RespCode :" x);
pardakht2();
//var x ="00";
/* if (x == 100) {
pardakht2();
}
else {
ErrorModal();
}*/
}
, error: function (xhr, ajaxOptions, thrownError) {
ErrorModal();
}
}); //ajax
}
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GPT-4 was manually vetted by our editorial team and was first featured on March 14th 2023.
24 alternatives to GPT-4 for Large Language Models
Pros and Cons
Pros
Accepts both image and text inputs
Emits text outputs
Achieves human-level performance on benchmarks
Large multimodal model
Trained on vast corpus of data
Generates coherent and contextually appropriate text
Capable of nuanced instruction handling
Capabilities tested on human-designed exams
Outperforms existing large language models
Available via API
Open-sourced evaluation framework
Improved reliability from predecessor
Exhibits enhanced creativity
Built for scalability
Capable of simulating professional tests
Tested on traditional machine learning benchmarks
Better performance on low-resource languages
Used internally for support, sales, content moderation
Significant reduction in hallucination
Improved scores on adversarial factuality evaluations
Scored highly on TruthfulQA benchmark
Can handle complex tasks
Augmentable with test-time techniques
Gives more accurate prediction in training performance
Supports customization of user experience
Outperforms state-of-the-art models on benchmarks
Shows impressive capability in multiple languages
Cons
Limited reliability
Hallucinates facts
Prone to reasoning errors
Strictly requires careful usage
Limited in high-stakes contexts
Issues with hallucinations
Inaccurate in factual evaluation
Poor performance on TruthfulQA
Q&A
What is GPT-4?
GPT-4 is a large multimodal model developed by OpenAI that is capable of accepting both image and text inputs, and emitting text outputs. It exhibits human-level performance on various professional and academic benchmarks. The AI has been designed to be more reliable, creative, and able to handle much more nuanced instructions than its predecessor, GPT-3.5.
What is the difference between GPT-4 and its predecessor, GPT-3.5?
The difference between GPT-4 and GPT-3.5 is primarily in their reliability, creativity, and ability to handle nuanced instructions. GPT-4 has been tested on a variety of benchmarks, and it has outperformed GPT-3.5 in many aspects. It is more capable at handling complex tasks, making it more reliable for a broader range of applications.
What is the performance of GPT-4 on professional and academic benchmarks?
GPT-4 has achieved impressive performance on professional and academic benchmarks. It has been used for simulating various exams that were initially designed for humans. For instance, GPT-4 passed a simulated bar exam scoring around the top 10% of test takers. GPT-4 was also evaluated on traditional benchmarks designed for machine learning models, where it considerably outperforms existing large language models and most state-of-the-art models.
Can GPT-4 process both image and text inputs?
Yes, GPT-4 can process both image and text inputs. It generates text outputs given inputs consisting of interspersed text and images. This allows the model to handle tasks in various domains, such as documents with text and photographs, diagrams, or screenshots.
What is the ChatGPT and API in context of GPT-4's capability?
The ChatGPT and API are channels through which GPT-4's text input capability is made available. Developers and users can interface with GPT-4's capabilities via these channels. These interfaces allow users to exploit the advanced capability of GPT-4 in a much more convenient and user-friendly way.
Is GPT-4 available for wider availability?
While GPT-4's text input capability is available now via ChatGPT and the API, the image input capability is currently being prepared for wider availability. The preparation for wider availability of the image capability is being executed through a collaboration with a single partner.
What is the role of OpenAI Evals in GPT-4's development?
OpenAI Evals is an open-source framework created by OpenAI for automated evaluation of AI model performance. The framework is part of GPT-4's development process. It allows anyone to report shortcomings in the models, providing valuable insights that guide further improvements in the AI model.
What is a 'multimodal model' in reference to GPT-4?
Being termed as a 'multimodal model', GPT-4 has the ability to process multiple types of inputs, specifically image and text inputs, and emit text outputs. This gives the model flexibility to handle much more complex and diverse tasks compared to models that can only handle one type of input.
How was GPT-4 trained?
GPT-4 has been trained on a deep learning stack that was rebuilt over the past two years. It was co-designed with Azure and trained on a supercomputer, specifically designed for the workload. The model was trained on a vast corpus of data to generate coherent and contextually appropriate text responses to various input types.
What exams has GPT-4 simulated?
GPT-4 simulated several exams originally designed for humans during its testing phase. Some of the exams simulated include the bar exam, the LSAT, SAT Math and Evidence-Based Reading & Writing, GRE Quantitative and Verbal, among others. GPT-4 performed impressively in these simulations, often scoring in the high percentiles.
What is GPT-4's relationship with traditional machine learning benchmarks?
GPT-4 was evaluated on traditional benchmarks designed specifically for machine learning models. In these evaluations, GPT-4 demonstrated superior performance, significantly outperforming existing large language models and most state-of-the-art models, some of which may include benchmark-specific crafting or additional training protocols.
How does GPT-4 handle nuanced instructions?
When it comes to handling nuanced instructions, GPT-4 shows improved capability over GPT-3.5. This improvement is evident when the complexity of tasks reaches a certain threshold, where GPT-4 proves to be more reliable and creative. This ability allows GPT-4 to handle tasks that require a high level of understanding and complexity.
What are the improvements in GPT-4 over previous models?
Comparing GPT-4 with previous models, there have been significant improvements in reliability, creativity, and the ability to process nuanced instructions. The model also shows better performance on various benchmarks and is even capable of producing human-like performance in some tasks. Moreover, it has vastly improved factuality, steerability, and adherence to guardrails.
Is GPT-4 being used in real-world applications?
Yes, GPT-4 is being used in real-world applications. Inside OpenAI, it has shown great impact on functions like support, sales, content moderation, and programming. It also assists humans in evaluating AI outputs, exemplifying a practical application of AI in process and quality control.
What about GPT-4's image input capability?
GPT-4 has an image input capability that allows it to generate text outputs given inputs consisting of interspersed text and images. However, the image input capability of GPT-4 is still in a research preview stage and is not currently available for public use.
How does GPT-4's performance compare on English vs other language benchmarks?
GPT-4 was tested on the MMLU benchmark which was translated into a variety of languages to test the model's capability in languages other than English. In 24 of 26 tested languages, GPT-4 outperformed the English-language performance of GPT-3.5 and other large language models. This suggests that GPT-4 has improved multilingual capability, even in low-resource languages like Latvian, Welsh, and Swahili.
What is the role of Azure in GPT-4's development?
Azure played a significant role in the development of GPT-4. Over the past two years, OpenAI and Azure co-designed a supercomputer ground-up specifically for the workload of GPT-4's deep learning stack. This collaboration facilitated the successful training and development of GPT-4.
How does GPT-4 handle conversation steering?
GPT-4 exhibits enhanced conversation steering capability. Developers and soon users can prescribe the AI’s style and task by describing them in the “system” message, allowing significant user experience customization within certain boundaries. This allows for effective conversation direction, moving away from the classic fixed style of its predecessors, achieving a more user-drive conversational steerability.
What is the function of system messages in GPT-4?
In GPT-4, system messages play a crucial role as they allow API users to customize user experiences within established boundaries. They enable the prescription of AI’s style and task, giving users a more tailored experience. While OpenAI acknowledges the limitations in the adherence to the bounds of system messages, they provide a significant level of customization of GPT-4's responses.
What limitations does GPT-4 have?
GPT-4, despite its advanced capabilities, has limitations similar to earlier GPT models. It can still 'hallucinate' facts and make reasoning errors, making it not fully reliable. While hallucinations have significantly reduced compared to previous models, GPT-4 requires careful handling, particularly in high-stakes contexts for accurate and fact-based outputs.
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