The Rise of Language Models in 2023 : Scripting the Future

In the dynamic landscape of language technology in 2023, this phenomenon has taken a monumental stride, marked not only by the continued dominance of GPT but also by the introduction of novel and improved iterations from industry leaders. From the forefront of innovation at companies like Google, OpenAI, Meta , Microsoft, and beyond, the latest developments in LLMs promise to redefine the way we interact with and harness the power of language in our rapidly evolving digital landscape. This article explores the notable breakthroughs, transformative applications, and the collective impact of LLM advancements that shaped the technological narrative in 2023.

Article Covers :

1. Key characteristics of Large Language Models (LLMs)
2. Business Use
3. Notable LLMs released in 2023
4. Small Language Model (SLMs) and business fitment
5. Future and expectations

Key characteristics of LLMs:

• Deep learning architecture: LLMs utilize deep learning techniques, particularly transformers, which allow them to learn complex patterns and relationships in the data.
• Fine-tuning: LLMs can be fine-tuned for specific tasks or domains, further enhancing their performance in particular areas.
• Adaptability: LLMs can adapt to new information and situations, making them versatile tools for various applications.
• Large size: LLMs are trained on vast datasets of text and code, often containing billions or even trillions of words. (petabytes of data)

Size matters a lot but there is a surprise : SML - "Small Language Model"

Business Use :

Text Generation: LLMs can create different kinds of creative text formats, like poems, code, scripts, musical pieces, emails, letters, etc. They can also generate summaries of factual topics or even write fictional stories.

Translation: LLMs can translate text between different languages with high accuracy, breaking down barriers in communication and access to information.

Question Answering: LLMs can provide comprehensive and informative answers to complex questions, drawing from their vast knowledge base and understanding of the world.

Code Generation: LLMs can generate code in various programming languages, aiding programmers in development tasks and automating routine coding processes.

Open-Domain Dialogue: LLMs can engage in natural and informative conversations with humans, responding thoughtfully to a wide range of prompts and questions.

Reasoning and Problem-Solving: LLMs can analyze information, identify patterns, and draw logical conclusions, helping to solve complex problems and make informed decisions.

Summarization: LLMs can condense large amounts of text into concise and informative summaries, saving users time and effort.

In recent years, large neural networks trained for language understanding and generation have achieved impressive results across a wide range of tasks with text, audio and video as well.

Most notable LLM models released in 2023 :

Google :

Gemini (Dec 2023) , a flexible model that is capable of running on everything from Google's data centers to mobile devices. To achieve this scalability, Gemini is being released in three sizes: Gemini Nano, Gemini Pro, and Gemini Ultra.

• Gemini Nano:?The Gemini Nano model size is designed to run on smartphones, specifically the Google Pixel 8. It's built to perform on-device tasks that require efficient AI processing without connecting to external servers, such as suggesting replies within chat applications or summarizing text.?
• Gemini Pro:?Running on Google's data centers, Gemini Pro is designed to power the latest version of the company's?AI chatbot, Bard. It's capable of delivering fast response times and understanding complex queries.?
• Gemini Ultra:?Though still unavailable for widespread use, Google describes Gemini Ultra as its most capable model, exceeding "current state-of-the-art results on 30 of the 32 widely-used academic benchmarks used in large language model (LLM) research and development." It's designed for highly complex tasks and is set to be released after finishing its current phase of testing.?

PaLM 2 (May 2023) : A Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction.

PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities.

OpenAI:

• GPT-4 (March 2023): GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks. For example, it passes a simulated bar exam with a score around the top 10% of test takers; in contrast, GPT-3.5’s score was around the bottom 10%.

Mistral AI :

Mistral 7B (Sept 2023) is Mistral AI’s first large language model (LLM), which has 7.3 billion parameters and has proven competitive with Meta’s Llama 2 13B, which has 13 billion parameters.?Mistral 7B is an auto-regressive language model that uses an optimized transformer architecture. It was trained on a new mix of publicly available online data, consisting of 2 trillion tokens from various domains and languages.

Meta :

• Llama 2 (July 2023) :Microsoft and Meta are expanding their longstanding partnership, with Microsoft as the preferred partner for Llama 2 with the support of a broad set of companies and people across tech, academia, and policy who also believe in an open innovation approach to today’s AI technologies.
• Through Hugging Face, you can try out the following versions of Llama 2: Llama 2 7B Chat / Llama 2 13B Chat / Llama 2 70B Chat

Falcon 180B : Falcon 180B (September 2023) is a super-powerful language model with 180 billion parameters, trained on 3.5 trillion tokens. It's currently at the top of the Hugging Face Leaderboard for pre-trained Open Large Language Models and is available for both research and commercial use..

This model performs exceptionally well in various tasks like reasoning, coding, proficiency, and knowledge tests, even beating competitors like Meta's LLaMA 2.

Among closed source models, it ranks just behind OpenAI's GPT 4, and performs on par with Google's PaLM 2 Large, which powers Bard, despite being half the size of the model.

• Tongyi Qianwen :Tongyi Qianwen, the latest proprietary large language model developed by Alibaba Cloud, is causing quite a stir in the AI landscape.As interest in large language models and generative AI continues to expand, accelerated by the hype around OpenAI, Alibaba’s new service will earn plenty of attention. This ChatGPT-style solution aims to compete with other would-be OpenAI killers, from Google’s Bard to the new Falcon 180B open-source model.The cloud giant used a massive corpus of Chinese and English text to train the system.?Initially, people suggested Tongyi could be trained with as many as 10 trillion parameters. Alibaba has released two 7-billion-parameter open-source models based on similar architecture.

Looking Forward:

The rapid advancements in LLM technology in 2023 have opened up exciting possibilities for various applications across diverse industries. From personalized education and healthcare solutions to content creation and intelligent assistants, LLMs are poised to revolutionize the way we interact with technology and information.

But we still have a question - Large Model is always better when it comes to language AI?

Well that's not true in all cases : As an example, Phi-2 has 2.7 billion parameters and demonstrates state-of-the-art performances against benchmark testing parameters such as common sense, language understanding and logical reasoning.

At Ignite 2023, Microsoft announced the newest iteration of the Phi Small Language Model (SLM) series termed Phi-2. This comes at a time when many industry members are voicing their opinions that smaller models are going to be more useful for enterprises in comparison to Large Language Models (LLMs).?Not only size and computational requirements but high cost, biases and inefficiencies are still key issues in LLMs.

--Microsoft loves SLMs - Satya Nadella, Chairman and CEO at Microsoft

With Mistral 7B and Phi-2 performance it can be said that industry will also move towards small language but high efficient models. Target should be to develop optimized models with less costly setup , less memory , fewer parameters and quick training bringing in transparency, data control and efficiency.

As we look to the future, the interplay between model size, functionality, and real-world applications will continue to shape the trajectory of language AI. Whether it's personalizing education, enhancing healthcare, fostering creativity in content creation, or assisting us intelligently, the possibilities seem boundless.

Future is not just about the size but the strategic integration of advanced features that holds the key to unlocking new dimensions of progress. As we embark on this transformative journey, we have to embrace the diversity of approaches and appreciate the unique strengths that each model brings to the table, propelling us towards a future where language AI becomes an indispensable and harmonious companion in our daily lives.

PS : I'm interested in hearing your perspectives on the direction you anticipate Large Language Models (LLMs) and Small Language Models (SLMs) will take in 2024.

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References:

https://www.zdnet.com/article/what-is-google-gemini/

https://www.infoq.com/news/2023/09/falcon-180b-llm/

https://ai.google/static/documents/palm2techreport.pdf

https://zapier.com/blog/llama-meta/

https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard

https://www.uctoday.com/unified-communications/what-is-tongyi-qianwen-alibabas-chatgpt-rival/