From GPT-3 to Future Generations of Language Models

Introduction

Large Language Models (LLMs) have revolutionized natural language processing, enabling computers to generate human-like text and understand context with unprecedented accuracy. In this article, we shall discuss what will be the future of language models? How LLMs will revolutionise the world? Among the notable LLMs, Generative Pre-trained Transformer 3 (GPT-3) stands as a significant milestone, captivating the world with its impressive language generation capabilities. However, as LLMs continue to evolve, researchers have been addressing the limitations and challenges of GPT-3, paving the way for future generations of even more powerful language models.

Here, we will explore the evolution of LLMs, starting from GPT-3 and delving into the advancements, real-world applications, and exciting possibilities that lie ahead in the field of language modeling.

Learning Objectives

• To understand various types of LLMs.
• To know about GPT3 and its base models.
• To gain insights into the advancement of LLMs.
• To learn to use the weights of LLM from Hugging Face and what finetuning is.

This article was published as a part of the Data Science Blogathon.

Different Types of LLMs

1. Base LLMs

Base LLMs serve as the foundational pre-trained language models that act as the starting point for a wide range of natural language processing (NLP) tasks. It predicts the next word based on text training data.

Applications

• Text Generation: LLMs excel at generating coherent and contextually relevant text, making them useful in content creation, creative writing assistance, and automated summarization.
• Question Answering: LLMs can read and comprehend text documents, enabling them to answer questions based on the provided information.
• Machine Translation: LLMs can improve the accuracy and fluency of machine translation systems, facilitating the translation of text between different languages.

2. Instruction Tuned LLMs

Instruction-tuned LLMs refer to language models that have undergone fine-tuning or specialization for specific tasks or instructions, aiming to comply with those particular instructions.

Base LLMs provide a broad understanding of language, whereas instruction-tuned LLMs are specifically trained to adhere to specific guidelines or instructions, rendering them more suitable for particular applications.

Applications

• Machine Translation: Instruction-Tuned LLMs can be fine-tuned on specific language pairs or domains to improve translation quality and accuracy.
• Sentiment Analysis: Instruction-Tuned LLMs can be fine-tuned to perform sentiment analysis more accurately by providing specific instructions or examples during training.
• Named Entity Recognition: Instruction-Tuned LLMs can be fine-tuned to detect named entities (e.g., persons, organizations, locations) with higher precision and recall.
• Intent Recognition: Instruction-Tuned LLMs can be fine-tuned to accurately recognize and understand user intents in applications like voice assistants or chatbots.

Both base LLMs and instruction-tuned LLMs play essential roles in language model development and NLP applications. Base LLMs provide a strong foundation with their general language understanding, while instruction-tuned LLMs offer a level of customization and specificity to meet the requirements of specific tasks or instructions.

By fine-tuning LLMs with specific instructions, prompts, or domain-specific data, Instruction-Tuned LLMs can provide enhanced performance and better alignment with specific tasks or domains compared to the base LLMs.

GPT-3: A Milestone in LLM Development

Generative Pre-trained Transformer 3 (GPT-3) has emerged as a groundbreaking achievement in the field of Large Language Models (LLMs). This transformative model has accumulated immense attention for its exceptional language generation capabilities and has pushed the boundaries of what was previously thought possible in natural language processing.

GPT 3 Base Models

GPT-3 models have the capability to understand and generate natural language. The GPT 3 base models are the only models that are available for finetuning.

It has the endpoint: /v1/completions

Using the GPT3 Davinci Model for Text Generation

The first task is to load your OpenAI API key in the environment variable and import the necessary libraries.

# Import necessary libraries
import openai
import os
import IPython
from dotenv import load_dotenv

load_dotenv()
# API configuration
openai.api_key = os.getenv("OPENAI_API_KEY")

This demonstrates how to generate text using OpenAI’s GPT-3 model, here davinci model. The prompt is used as a starting point, and the ‘openai.Completion.create()’ method is used to make an API call to GPT-3 for text generation. The generated text is then printed to the console, allowing users to see the output of the text generation process.

# Define a prompt for text generation
prompt = "Once upon a time"

# Generate text using GPT-3
response = openai.Completion.create(
    engine='davinci',
    prompt=prompt,
    max_tokens=100  # Adjust the desired length of the generated text
)

# Print the generated text
print(response.choices[0].text.strip())

Output

I worked as a health services coordinator faced with the chore of creating a weight chart to hand out to our clients. It had 7 categories, plus a title. This was a challenge.

Need for other LLMs despite GPT3

While GPT-3 is a powerful and versatile language model, there is still a need for other LLMs to complement and enhance the capabilities of GPT-3. Here are a few reasons why other LLMs are important:

• GPT-3 is a general-purpose language model, but specialized LLMs can provide better performance and accuracy for specific use cases
• Smaller and more efficient LLMs offer a cost-effective alternative to the computationally expensive GPT-3, making deployment more accessible.
• LLMs trained on specific datasets or incorporating domain-specific knowledge provide the contextual understanding and more accurate results in specialized domains.
• Continued research and development in the field of LLMs contribute to advancements in natural language processing. understanding.

Though GPT-3 is a remarkable language model, the development and utilization of other LLMs are necessary to cater to specialized domains, improve efficiency, incorporate domain-specific knowledge, address ethical concerns, and drive further research and innovation in the field of natural language processing.

Advancements in LLM beyond GPT-3

The evolution of LLMs doesn’t stop at GPT-3. Researchers and developers are continuously working on advancements to address the limitations and challenges. Recent models, such as GPT-4, Megatron, StableLM, MPT, and many more have built upon the foundations laid by GPT-3, aiming to improve performance, efficiency, and handling of biases.

For instance,

• GPT-4 focuses on reducing computational requirements while maintaining or improving the quality of language generation.
• Megatron emphasizes scalable model training, enabling the training of even larger LLMs efficiently.
• StableLM targets stability issues in large models, ensuring consistent and reliable performance.

These advanced LLMs have demonstrated promising results. For example, Megatron has achieved state-of-the-art results in various NLP benchmarks. StableLM has addressed issues related to catastrophic forgetting, enabling continuous learning in large-scale models. These advancements pave the way for more efficient, capable, and reliable LLMs that can be deployed in a wider range of applications.

Recent LLMs Developments in 2023

The issue with LLMs for commercial use is that they might not be opensource or prohibited for use. As a result, businesses might not be able to use them at all or might have to pay to do so. For reasons like transparency and the flexibility to change the code, some businesses may also prefer to use opensource models.

Commercially Available Open-Source Language Models

There are a number of commercially available open-source language models.

• Pythia:  It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. The checkpoints for every model size are available in the hugging face. You can also check out the implementation on GitHub.
• StableLM Alpha: StableLM-Tuned-Alpha is a collection of 3B and 7B parameter decoder-only language models built on top of the StableLM-Base-Alpha models and further fine-tuned on various chat and instruction-following datasets. The checkpoints for both model sizes are available in the hugging face. You can also check out the implementation on GitHub.
• H2oGPT: h2oGPT is a fine-tuning framework for large language models (LLMs) and a chatbot UI with document(s) question-answer capabilities. Documents provide context relevant to the instruction, which helps to ground LLMs against hallucinations. You can check out the implementation on GitHub.
• Dolly: Dolly-v2-12b, is an instruction-following large language model trained on the Databricks machine learning platform. It is not a state-of-the-art model, but it demonstrates unusually high-quality instruction following behavior that is not typical of the foundation model on which it is built. You can check out the implementation on GitHub.
• Bloom: BLOOM is an autoregressive Large Language Model (LLM) trained on massive volumes of text data. As a result, it can generate meaningful text in 46 languages and 13 programming languages that are nearly indistinguishable from human-written material. You can check out the checkpoints for Bloom on Hugging Face.
• Falcon: Falcon-40B is a 40B parameters, causal decoder-only model. It outperformed LLaMA, StableLM, RedPajama, MPT, and many other models. It is a pre-trained model, which should be finetuned further for most use cases. You can check out the model on Hugging Face.

How to use weights of LLMs from Hugging Face?

We will utilize Falcon7b, a pre-trained causal decoder-only model, which typically requires further fine-tuning for most use cases. However, for text generation, it has demonstrated superior performance compared to various other models.

Import Necessary Libraries

!pip install transformers
!pip install torch

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

Load Model and Tokenizer

The next step is to instantiate an AutoTokenizer object and load the tokenizer as well as the model for the pre-trained Falcon model.

model = "tiiuae/falcon-7b-instruct" 
tokenizer = AutoTokenizer.from_pretrained(model)

Build the Model Pipeline Using Hugging Face Transformers Pipeline

It creates a text generation pipeline using the Transformers library. It specifies the task as “text-generation” and requires a pre-trained model and tokenizer. The computations are configured to utilize a 16-bit floating-point number data type.

!pip install einops
!pip install accelerate

pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)

Model Inference

The task at hand is to utilize the built pipeline to print the result. The ‘prompt’ variable contains the initial text that serves as a starting point. We configure the pipeline to generate a maximum of 200 tokens, enable sampling, and consider the top 10 probable tokens at each step.

prompt = "Write a poem about Elon Musk firing Twitter employees"

sequences = pipeline(
    prompt,
    max_length=200,
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)

for seq in sequences:
    print(f"Result: {seq['generated_text']}")
    

Output

Future Possibilities and Ethical Considerations

The future of LLMs is promising, with countless possibilities awaiting exploration. Advancements in LLMs hold the potential to create virtual assistants that are indistinguishable from humans, revolutionizing customer service and human-computer interactions. Enhanced language understanding and generation capabilities can lead to more seamless and immersive virtual reality experiences. LLMs can also play a crucial role in bridging language barriers and fostering global communication.

However, as LLMs continue to evolve, ethical considerations become paramount.

• Transparency, accountability, and bias mitigation techniques are crucial to ensure the responsible development and use of LLMs.
• Strict guidelines and regulations are necessary to address issues of misinformation, data privacy, and the potential for misuse.
• Additionally, collaboration between researchers, developers, and policymakers is vital to foster ethical practices and safeguard the interests of individuals and society as a whole.

Finetuning LLM

The fine-tuning process involves training the base LLM on task-specific datasets, where the model learns to generate responses or outputs that align with the desired instructions or guidelines. This fine-tuning process allows the model to adapt its language generation capabilities to meet the specific requirements of the task at hand.

Instruction-tuned LLMs find particular utility in scenarios that demand a high degree of control or adherence to specific guidelines. For instance, in chatbot applications, fine-tuning instruction-tuned LLMs allows the generation of responses that are more contextually appropriate, specific to the domain, or aligned with desired conversation guidelines.

By fine-tuning base LLMs with task-specific instructions, developers can create a more specialized and targeted language model. This process enhances the model’s performance and enables it to generate tailored outputs that excel in specific applications.

Real-world Examples of Evolved LLMs

The evolution of LLMs brings forth a multitude of real-world applications with significant impact.

• Evolved LLMs can revolutionize customer support systems by providing personalized and context-aware responses to user queries.
• Further streamlining of content creation processes enables faster and more engaging content generation across platforms.
• Language translation can become more accurate and nuanced, facilitating cross-cultural communication.

Moreover, evolved LLMs hold potential in the fields of healthcare, legal, and education.

• In healthcare, these models can assist in medical diagnosis, recommending treatments based on patient symptoms and medical histories.
• In the legal sector, LLMs can aid in legal research, analyzing vast amounts of legal documents and providing insights for cases.
• In education, LLMs can contribute to personalized learning experiences, offering tailored educational content to students based on their specific needs and learning styles.

Conclusion

The evolution of LLMs, from GPT-3 to future generations, marks a significant milestone in the field of natural language processing. These advanced models have the potential to revolutionize various industries, streamline processes, and enhance human-computer interactions.

Nevertheless, advancements in language models come with limitations, challenges, and ethical considerations that necessitate attention. It is crucial to responsibly develop and deploy large language models (LLMs), supported by ongoing research and collaboration. These efforts will shape the future of language models, enabling us to reap their benefits while mitigating potential risks. The journey of LLMs continues, holding great promise for the advancement of AI and the transformation of our interactions with technology.

Key Takeaways

• The evolution of LLMs represents a significant milestone in natural language processing, enabling revolutionary applications and improved human-computer interactions.
• It is important to recognize and address the limitations and challenges associated with LLMs, such as bias and ethical considerations, to ensure responsible development and deployment.
• Continuous research, collaboration, and responsible use of LLMs will shape the future of AI, unlocking transformative possibilities in language understanding and interaction.

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Babina Banjara 17 Aug 2023