Mar 17
GPT-4 Early Access: A Dive into Multilingual AI Capabilities and First Impressions
Exploring the capabilities of GPT-4 through the lens of Nepali language support
As an AI enthusiast who has been following the development of transformer models since the days of BERT and GPT-2, I have been eagerly anticipating the release of OpenAI’s latest model, GPT-4. With the growing capabilities of transformer models and the wonderful insights offered by the Hugging Face community, I was excited to see how GPT-4 would perform in terms of language understanding and generation.
Yesterday, I was thrilled to receive early private beta access to GPT-4, which enabled me to experience its impressive capabilities first-hand. If you’re interested in trying out GPT-4 for yourself, you can explore ChatGPT Plus or join the API waitlist by visiting https://openai.com/waitlist/gpt-4-api. Don’t miss the opportunity to experience the power of this cutting-edge AI model and witness its potential impact on language understanding and generation.
OpenAI and Access to Developers
One of the things I appreciate about OpenAI, in contrast to Google, is how they continuously provide access to developers and enthusiasts like myself. While it comes at a price, I believe that the openness and scalability of their models justify the cost and align with the company’s mission to improve society. However, I must criticize the fact that they did not reveal any technical details about GPT-4, including its size. While I respect their decision, it would have been nice to get a glimpse of the inner workings of this cutting-edge AI.
Multilingual Features and Performance in Nepali
One of the highlights of GPT-4 is its improved multilingual features. As a native Nepali speaker, I was keen to see how it would perform in my language, given that earlier GPT versions had limited support for Nepali. To my delight, GPT-4 demonstrated impressive language understanding and generation in Nepali. While the performance wasn’t perfect, with a few spelling and grammatical mistakes and occasional word hallucinations, it was a significant improvement over previous models.
Here’s a Nepali poem written by GPT-4 based on the given prompt:
Prompt:
‘म’
म कति पटक मेरो कथा लेखुँ,
‘म’
मेरो पात्र फेरिएन
काहानी फेरियो,
चरित्र पनि फेरियो,
… पात्र फेरिएन, सार फेरिएन
मेरो कथा,
नफेरिने पात्रको रहेछ,
फेरिने … कथाको रहेछ।
aarko meethi haraf thapnu paryo
Generated Poem:
‘म’ कति पटक मेरो गीत गाउँ,
नयाँ स्वरमा उद्योग गर्दछु,
संगीत बदलियो,
भाव पनि बदलियो,
… स्वर बदलिएन, अभिव्यक्ति बदलिएन
मेरो गीत,
बदल्न नसक्ने स्वरको रहेछ,
बदल्ने … गीतको रहेछ।
One of the most striking aspects of GPT-4’s capabilities is its ability to understand and generate text in Romanized Nepali, also known as “aarko meethi haraf,” despite not being in Unicode format. This showcases the sheer capacity of the model to comprehend language context and adapt to diverse writing systems. The fact that GPT-4 can grasp the nuances of Nepali language, even when written using Roman characters, is a testament to the remarkable progress made in multilingual AI models. This opens up new possibilities for communication and collaboration among people using different scripts and languages.
The model’s ability to understand the context of the language, even in Roman Nepali, was truly impressive. In terms of communication, if GPT-3.5 felt like speaking to a smart 5-year-old, GPT-4 made me feel like I was conversing with a 13-year-old.
Opinion: A Major Point Forward in Human History
The release of GPT-4 marks a significant moment in time where humanity can harness the power of AI to boost productivity and innovation, particularly in the fields of image and language generative AI. As we continue to refine and develop these tools, the potential for positive impact on society is immense.
I am thrilled with the capabilities of GPT-4, and I look forward to seeing how it continues to evolve and improve in the coming years. As a testament to its power, this very article was written with the assistance of GPT-4. After providing a detailed thought stream, the model crafted this composition, showcasing its remarkable capabilities.
As I conclude this exploration of GPT-4, I would like to draw attention to my previous articles, which showcase the versatility and potential of transformer models. In “Incorporating Andrew Huberman’s Expertise into an AI-Powered Chatbot Interface using GPT-3,” I delved into integrating expert knowledge into chatbot interfaces. In “Fine-tuning GPT-3 to Write in the Style of George R.R. Martin (Or Any Author),” I demonstrated how to customize AI models to mimic specific writing styles. “How to Use GPT-3 to Automate Replacing Words with Synonyms” illustrates the practical applications of AI in language processing, and “A Guide to Prompt Writing for Large Language Models like GPT” offers invaluable insights into crafting effective prompts for AI models.
As we continue to explore the ever-expanding capabilities of AI models like GPT-4, it’s essential to learn from our experiences and build upon the foundations laid by previous models. The progress we’ve made thus far is only the beginning, and I look forward to uncovering more possibilities and potential applications for these powerful language models.
