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OpenAI’s GPT-3, with its impressive capabilities but flaws, was a landmark in AI writing that showed AI could write like a human. The next version, probably GPT-4, is expected to be revealed soon, possibly in 2023. Meanwhile, OpenAI has launched a series of AI models based on a previously unknown “GPT-3.5,” which is an improved version while we compare GPT-3 vs. GPT-3.5. In this blog, let’s uncover more about GPT-3 vs. GPT-3.5 and how GPT-3.5 stands out as an improved version of GPT-3.
GPT-3 is a deep learning-based language model that generates human-like text, code, stories, poems, etc. Its ability to produce diverse outputs has made it a highly talked-about topic in NLP, a crucial aspect of data science.
Open AI introduced GPT-3 in May 2020 as the follow-up to its earlier language model, GPT-2. GPT-3 is considered a step forward in size and performance, boasting 175 billion trainable parameters, making it the largest language model to date. The features, capabilities, performance, and limitations of GPT-3 are thoroughly explained in a 72-page research paper.
Language models are simply statistical tools that anticipate the next word(s) in a sequence by computing the probability distribution of a sequence of words. Language models are typically trained on large text corpora, such as books, news articles, and web pages. The model uses this training data to learn the statistical patterns and relationships between words in the language and then uses this knowledge to predict the next word in a sequence or to classify the sentiment of a given text. They are used in a variety of NLP tasks, including:
A widely used NLP encoding method is Word2Vec, introduced in 2014. However, a breakthrough in language modeling occurred in 2019 with the advent of the “transformer” architecture.
What’s different in GPT-3.5?
Recently GPT-3.5 was revealed with the launch of ChatGPT, a fine-tuned iteration of the model designed as a general-purpose chatbot. It made its public debut with a demonstration showcasing its ability to converse on various subjects, including programming, TV scripts, and scientific concepts.
While contemplating GPT-3 vs. GPT-3.5, OpenAI states that GPT-3.5 was trained on a combination of text and code before the end of 2021. Like its predecessor GPT-3 and other text-generating AI models, it learned to understand the connections between sentences, words, and parts of words by consuming vast amounts of content from the web, such as hundreds of thousands of Wikipedia, social media posts, and news articles.
Instead of releasing GPT-3.5 in its fully trained form, OpenAI utilized it to develop several systems specifically optimized for various tasks, all accessible via the OpenAI API. One of these, text-davinci-003, is said to handle more intricate commands than models constructed on GPT-3 and produce higher quality, longer-form writing.
OpenAI data scientist Jan Leike stated that text-davinci-003 is comparable to InstructGPT, a series of GPT-3-based models that OpenAI introduced earlier this year. These models are designed to minimize the generation of problematic text, like toxic or highly biased content, while better adhering to a user’s intentions. Leike mentioned in a tweet that text-davinci-003 and GPT-3.5 have “higher scores on human preference ratings” and fewer “severe limitations.”
There is some anecdotal evidence to support this. Data scientists at Pepper Content, a content marketing platform, have noted that text-davinci-003 “excels in comprehending the context behind a request and producing better content as a result” and hallucinates less than models based on GPT-3. In text-generating AI, hallucination refers to creating inconsistent and factually incorrect statements.
GPT-3.5 can be accessed through the OpenAI Playground, a user-friendly platform. The interface allows users to type in a request, and there are advanced parameters on the right side of the screen, such as different models with unique features. The latest model, text-davinci-003, has improved output length compared to text-davinci-002, generating 65% longer responses. The output can be customized by adjusting the model, temperature, maximum length, and other options that control frequency, optionality, and probability display.
The OpenAI Playground offers a user-friendly interface to access the model. It enables users to input requests directly into the front end. Multiple models have different features, including the latest text-davinci-003, which generates 65% longer outputs than its previous version, text-davinci-002.
GPT-3 vs. GPT-3.5: Is GPT-3.5 better than GPT-3?
It’s unclear what makes GPT-3.5 win the debate of GPT-3 vs. GPT-3.5 in specific areas, as OpenAI has not released any official information or confirmation about “GPT-3.5”. A request for comment from OpenAI was declined. However, it is speculated that the improvement could be due to the training approach used for GPT-3.5. Like InstructGPT, GPT-3.5 was trained with human trainers who evaluated and ranked the model’s prompt responses. This feedback was then incorporated into the model to fine-tune its answers to align with the trainers’ preferences.
Despite its training approach, GPT-3.5 is not immune to the limitations inherent in modern language models. It relies solely on statistical patterns in its training data rather than truly understanding the world. As a result, it is still susceptible to “making stuff up,” as pointed out by Leike. Additionally, its knowledge of the world beyond 2021 is limited as the training data becomes more scarce after that year. Furthermore, the model’s mechanisms to prevent toxic outputs can be bypassed.
GPT-3.5 and its related models demonstrate that GPT-4 may not require an extremely high number of parameters to outperform other text-generating systems. Parameters learned from historical data and determined by a model’s skill are usually used to predict the size of future models. Some predictions suggest GPT-4 will have 100 trillion parameters, significantly increasing from GPT-3’s 175 billion. However, advancements in language processing, like those seen in GPT-3.5 and InstructGPT, could make such a large increase unnecessary.
In conclusion, language generation models like ChatGPT have the potential to provide high-quality responses to user input. However, their output quality ultimately depends on the quality of the input they receive. If the input is poorly structured, ambiguous, or difficult to understand, the model’s response may be flawed or of lower quality. Furthermore, machine learning technologies have limitations, and language generation models may produce incomplete or inaccurate responses. It’s important for users to keep these limitations in mind when using these models and to always verify the information they provide. While comparing GPT-3 vs. GPT-3.5, GPT-3.5 may provide more accurate and coherent responses, it’s still crucial to remember that these models are imperfect, and their output depends on their input quality.
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Why is GPT-3 so useful for businesses?
GPT-3, with its advanced language processing capabilities, offers significant utility to businesses by providing enhanced natural language generation and processing capabilities. Also, it can assist in automating various business processes, such as customer service chatbots and language translation tools, leading to increased operational efficiency and cost savings. Additionally, GPT-3’s ability to generate coherent and contextually appropriate language enables businesses to generate high-quality content at scale, including reports, marketing copy, and customer communications. These benefits make GPT-3 a valuable asset for businesses looking to optimize their language-based operations and stay ahead in today’s increasingly digital and interconnected business landscape.
What was GPT-3.5 trained on?
GPT-3.5 is a large language model based on the GPT-3 architecture. Like its predecessor, it was trained on a massive corpus of text data from diverse sources, including books, articles, websites, and other publicly available online content. The training dataset for GPT-3.5 was curated to include various topics and writing styles, allowing the model to understand natural language patterns and structures efficiently. This extensive training has enabled GPT-3.5 to achieve remarkable language processing capabilities, including generating human-like responses to complex prompts and tasks. It is a powerful tool for various language-based applications.
What are the capabilities of GPT-3.5?
The various capabilities of GPT-3.5 are outlined below as follows:
It can perform various language-based tasks, including translation, summarization, question-answering, sentiment analysis, and creative writing.
GPT-3.5’s sophisticated language generation abilities allow it to produce coherent and contextually appropriate responses to complex prompts and queries.
Also, the capabilities of GPT-3.5 makes it valuable for applications such as chatbots, virtual assistants, and content-generation tools.
GPT-3.5 has demonstrated strong performance on diverse language-based benchmarks, suggesting its potential for deployment in various real-world use cases.
Its capabilities are based on extensive training on a massive corpus of text data from diverse sources, allowing it to develop a comprehensive understanding of natural language patterns and structures.
How much does it cost to create a GPT-3 based application?
The cost of developing an application that leverages the advanced capabilities of GPT-3 may vary significantly, depending on several factors that include the app’s scope and complexity, required integrations, platform support, and the level of expertise of the development team. Additionally, the cost of utilizing GPT-3 API in the application will be a significant consideration. Moreover, this is typically charged per request or monthly subscription, depending on the specific usage and the API provider.
Don’t hesitate to contact us for an accurate estimate for developing an application incorporating GPT-3. Our team of experts will be delighted to provide you with a comprehensive assessment of the project’s requirements and associated costs.
How long would it take to launch a GPT-3 based application?
The costs associated with creating a GPT-3 application may be subject to variation based on a range of factors, including the complexity of the language model, the storage requirements for data hosting, and the level of advanced functionality required. Projects with greater complexity and sophistication typically require additional time and resources from a larger development team, leading to higher overall development costs.