GPT-3.5 vs. GPT-4: Unveiling the Power of the Next-Generation Language Models

This blog post was written by Brain John Aboze as part of the Deepchecks Community Blog. If you would like to contribute your own blog post, feel free to reach out to us via blog@deepchecks.com. We typically pay a symbolic fee for content that's accepted by our reviewers.

Introduction

Natural Language Processing (NLP) is a field within data science that empowers computers to grasp and interpret human language more effectively. At the heart of NLP, we find language models and computational linguistics, which facilitate the acquisition of grammar rules and linguistic patterns. Language models play a crucial role in AI by enabling machines to comprehend and process human language excellently. This capability is significant for AI systems that interact with humans, like chatbots and virtual assistants. Language modeling equips computers with the ability to learn and anticipate language inputs, facilitating precise and seamless understanding of natural language.

Source: Andrew Neel

Source: Andrew Neel

There are two primary language modeling approaches: Statistical and neural models. Statistical models, as the name would suggest, focus on using statistics for natural language processing, while neural models, on the other hand, use neural networks. GPT-3.5 and GPT-4 are both LLMs developed by OpenAI based on a neural architecture known as Generative Pretrained Transformers (GPT). They are both trained on massive datasets of text and code, allowing them to generate human-quality text, translate languages, and write different kinds of creative content. In this article, we will compare and contrast GPT-3.5 and GPT-4.

Among the most advanced language models available today are the Generative Pretrained Transformers(GPT) versions: GPT-3.5 and GPT-4. These large language models (LLMs) have been trained on massive datasets of text and code, enabling them to generate high-quality text, perform language translation, and even produce creative content. In this article, we delve into a comprehensive comparison of GPT-3.5 and GPT-4, shedding light on their advancements, differences, and the implications they bring forth.

GPT-3.5 vs. GPT-4: Key Differences

These LLMs have revolutionized our interactions with computers by serving as incredible tools for processing information. The release of GPT-3 in November 2020 marked a significant improvement over its predecessor, GPT-2. Furthermore, in March 2023, GPT-4 was introduced, surpassing even the potency of GPT-3. Now, let’s delve into the key distinctions between them:

  • Parameters: In language models, parameters refer to the model’s fundamental settings that define the model’s behavior and performance. The number of parameters is a measure of the complexity of the model. A larger number of parameters means the model is more complex and can learn more complex relationships between words and define the model’s understanding of grammar, syntax, semantics, and context. GPT-4 sets a new milestone with a staggering 175 trillion parameters, surpassing the 175 billion parameters of GPT-3.5. This substantial leap in parameter count promises significant advancements in generating coherent and contextually appropriate text, enhancing the model’s language understanding and natural language processing abilities. However, it is important to note that the increased parameter count also comes with higher computational requirements.
  • Modality: GPT-4 represents a significant leap forward as a multimodal model, surpassing GPT-3.5’s ability to process and generate outputs for text and image, in contrast to the unimodal processing and text generation limitations of GPT-3.5. With its multi-modality, GPT-4 demonstrates remarkable proficiency in comprehending and interpreting images and graphics, surpassing GPT-3.5. It excels in generating textual descriptions of visual content and can even generate visual elements based on textual descriptions. This enhanced image and graphics understanding of GPT-4 opens up new possibilities for generating rich and accurate visual content in alignment with textual input. GPT-4’s multimodal capabilities empower it to address complex problems and deliver more precise outputs.
  • Training data: GPT-4 and GPT-3 exhibit notable differences, primarily in the amount of data they were trained on. GPT-4 outperforms GPT-3 by training on a more expansive dataset. While GPT-3 employed approximately 45 terabytes of text data from various sources like Wikipedia and books, GPT-4 harnesses a more diverse and extensive dataset of 1 petabyte. This augmented training data empowers GPT-4 to yield more accurate results than its predecessor. Moreover, GPT-4 benefits from a more recent knowledge cutoff around September 2021, granting it access to up-to-date information and a better understanding of recent global developments than GPT-3, which had a knowledge cutoff around June 2020. It is important to note that larger models, such as GPT-4, bring forth enhanced capabilities. However, they also entail higher costs in terms of training and serving and increased latency.
Deepchecks For LLM VALIDATION

GPT-3.5 vs. GPT-4: Unveiling the Power of the Next-Generation Language Models

  • Reduce Risk
  • Simplify Compliance
  • Gain Visibility
  • Version Comparison
TRY LLM VALIDATION
  • Performance: Performance is crucial to evaluating a language model, measuring its ability to comprehend input and generate meaningful responses. GPT-4 showcases superior performance compared to GPT-3.5, exhibiting outputs that are more reliable, creative, and adept at handling nuanced instructions. With a more extensive vocabulary and multimodal capabilities, GPT-4 achieves enhanced accuracy in processing and generating text, code, and images. It excels in synthesizing information from diverse sources, creating coherent narratives with improved linguistic refinement. GPT-4 surpasses GPT-3.5 regarding token limits, allowing the former to generate longer and more complex text than the latter. GPT-4 introduces enhanced steerability, granting users greater control over the generated output. It enables the specification of text styles and focuses on specific topics, allowing for a more tailored and precise language generation experience. Moreover, GPT-4’s multilingual capability empowers it to process and generate text in multiple languages, a feature lacking in GPT-3.5. The overall performance of GPT-4 surpasses its predecessor, GPT-3.5, in various aspects of language modeling.
Exam results benchmark, source OpenAI

Exam results benchmark, source OpenAI

GPT-4’s evaluation of standard machine learning benchmarks demonstrated outstanding performance, surpassing both existing large language and state-of-the-art (SOTA) models. GPT-4’s achievements stand out even more as these benchmarks often require fine-tuning or additional training protocols. The exceptional capabilities of GPT-4 are evident from its ability to outperform other advanced language models, underscoring its position as a cutting-edge model in the field.

Traditional machine learning models benchmarks, source OpenAI

Traditional machine learning models benchmarks, source OpenAI

  • Mitigation of Inappropriate or Biased Responses: GPT-4 surpasses GPT-3.5 in its efforts to address the challenge of inappropriate or biased responses. It incorporates advanced techniques and filters to actively mitigate the generation of harmful or offensive language. This enhancement ensures the generated text is more neutral, objective, and aligned with ethical standards. OpenAI reports that GPT-4 exhibits a significantly lower rate of incorrect behavior than GPT-3.5 and GPT-3. According to their data, GPT-4 has an incorrect behavior rate of 0.02%, while GPT-3.5 has a rate of 0.07%, respectively. In practical terms, GPT-4 generates text that violates OpenAI’s content policy or user preferences only two times out of 10,000 completions, whereas GPT-3.5 does so seven times. These findings highlight GPT-4’s improved ability to avoid generating harmful or inappropriate text, further reinforcing its commitment to responsible and unbiased language generation.
  • Pricing: OpenAI provides multiple ways to access ChatGPT, including a web interface and APIs for integration into custom software solutions. OpenAI offers API access with a range of language models to cater to diverse use cases. The pricing structure operates on a pay-as-you-go basis, with charges based on the number of tokens processed per thousand. The free web version relies on GPT3.5, while the more robust GPT4 model is exclusively available through paid plans. It’s important to be aware that the free version of ChatGPT has certain limitations, as it supports only text-based interactions and is subject to predefined usage thresholds. During times of high demand, access may also be restricted. Additionally, it’s worth noting that free services often rely on user data for model training. For those seeking enhanced functionality and capabilities, OpenAI offers ChatGPT Plus, formerly ChatGPT Pro, as a subscription plan priced at $20 per month.
Upgrade your plan to ChatGPT Plus, Source Author

Upgrade your plan to ChatGPT Plus, Source Author

Subscribers to this plan can leverage the advanced GPT4 model, benefiting from priority access even during peak server load and experiencing faster response times. Early access to new features is another advantage. The speed, accuracy, and image processing capabilities provided by ChatGPT Plus make the $20 monthly fee a justifiable investment for businesses. In summary, OpenAI caters to different user preferences by providing both free and paid options to access ChatGPT. Subscribing to the paid plan unlocks additional benefits and harnesses the power of the more advanced GPT4 model.

Future Enhancements for ChatGPT

The future development of ChatGPT encompasses several key areas of focus for significant improvement:

  • Enhancing Neutrality: ChatGPT aims to improve its understanding of context and respond more neutrally and unbiasedly. This involves refining the model’s ability to provide objective and balanced responses without favoring any particular viewpoint.
  • User Understanding: Advancements in ChatGPT will prioritize comprehending the user’s identity, including their characteristics, location, and communication style. This deeper understanding will enable more personalized and contextually appropriate interactions.
  • External Integrations: ChatGPT will expand its integration capabilities across diverse platforms, such as web interfaces, APIs, and integration with robotic systems. This broad compatibility will allow for the seamless incorporation of ChatGPT into various applications and devices.
  • Long-Term Memory: Improvements will be made to ChatGPT’s capacity to remember and recall past interactions. This enhancement will facilitate more coherent and context-aware conversations over extended periods, enhancing the user experience and conversation continuity.
  • Minimizing Hallucination: Efforts will be focused on reducing instances where ChatGPT generates responses based on false or incorrect information. Ensuring the model remains grounded in factual accuracy is vital to building trust and reliability.
  • Ethical Considerations: ChatGPT development will prioritize addressing ethical concerns associated with AI language models. This includes mitigating potential biases, adhering to ethical guidelines, and ensuring responsible AI development practices are followed throughout the model’s lifecycle.
  • Security Risks: ChatGPT will thoroughly examine to identify and address potential security risks. Robust security measures and precautions will be implemented to prevent malicious use or exploitation of the model, safeguarding user privacy and data.

ChatGPT will continually enhance its overall effectiveness, reliability, and responsible deployment across various domains and applications by making significant progress in these areas.

Conclusion

While Google revolutionized internet search, ChatGPT has emerged as a significant leap forward in our approach to finding answers online. Gone are the days of sifting through search results; with models like GPT-3.5 and GPT-4, immediate responses are at our fingertips. GPT-4, with its multimodal capabilities, takes language processing to new heights by effectively handling both text and image inputs. Its exceptional image and graphics understanding allows for generating accurate textual descriptions of visual content and even creating graphical elements based on textual input. This breakthrough opens exciting possibilities for creating rich, contextually aligned visual content. Furthermore, GPT-4 significantly reduces inappropriate or biased responses, improves steerability, and achieves human-level performance on various benchmarks. Its linguistic finesse, creativity, coherence, and complex problem-solving abilities establish it as a cutting-edge language model.

As we look ahead, the continuous development and refinement of language models will undoubtedly shape the future of natural language processing and human-computer interaction. With each iteration, these models redefine the boundaries of what is achievable, empowering us to leverage AI-driven technologies for more fluent comprehension, processing, and communication. The journey from GPT-3.5 to GPT-4 is just one step in this exciting evolution, and we eagerly anticipate future advancements that will further transform the field of language modeling.

Deepchecks For LLM VALIDATION

GPT-3.5 vs. GPT-4: Unveiling the Power of the Next-Generation Language Models

  • Reduce Risk
  • Simplify Compliance
  • Gain Visibility
  • Version Comparison
TRY LLM VALIDATION

Recent Blog Posts

LLM Evaluation With Deepchecks & Vertex AI
LLM Evaluation With Deepchecks & Vertex AI
The Role of Root Mean Square in Data Accuracy
The Role of Root Mean Square in Data Accuracy
5 LLMs Podcasts to Listen to Right Now
5 LLMs Podcasts to Listen to Right Now