What are the Large Language Models ?
An LLM, or Large Language Model, is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large datasets to understand, summarize, generate, and predict new content. LLMs are trained on massive datasets of text and code, and they can learn to perform a variety of tasks, including:
- Text summarization: LLMs can be used to summarize long pieces of text into shorter, more concise versions.
- Text generation: LLMs can be used to generate new text, such as poems, code, scripts, musical pieces, email, letters, etc.
- Translation: LLMs can be used to translate text from one language to another.
- Question answering: LLMs can be used to answer questions about text.
- Code generation: LLMs can be used to generate code, such as Python, Java, or C+
Applications and Benefits of Large Language Models
Large language models (LLMs) are a type of artificial intelligence (AI) that are trained on massive datasets of text and code. This allows them to understand and generate human-like text, and to perform a variety of tasks, including:
- Natural language understanding: LLMs can understand the meaning of text, and to answer questions about it. For example, they can be used to answer customer questions about products or services, or to summarize news articles.
- Natural language generation: LLMs can generate human-like text, such as essays, articles, or even creative content like poems or stories. This can be used for a variety of purposes, such as content marketing, customer service, or even entertainment.
- Translation: LLMs can translate text from one language to another, with a high degree of accuracy. This can be used to translate websites, documents, or even conversations.
- Summarization: LLMs can summarize text, extracting the most important information and presenting it in a concise format. This can be used to save time when reading long documents, or to create summaries of news articles or blog posts.
- Question answering: LLMs can answer questions about the text, even if the questions are open-ended or challenging. This can be used to provide customer support or to help students with their homework.
Chatbots: LLMs can be used to create chatbots that can have natural conversations with humans. This can be used for customer service, education, or even entertainment.
These are just a few of the many applications of LLMs. As LLMs continue to develop, we can expect to see even more innovative and exciting applications in the future.
Here are some additional benefits of using large language models:
- Increased efficiency: LLMs can automate tasks that would otherwise require human intervention, such as summarizing text or answering questions. This can free up human workers to focus on more creative or strategic tasks.
- Improved accuracy: LLMs can be trained on massive datasets of text and code, which allows them to learn the nuances of human language. This can lead to improved accuracy in tasks such as translation, question answering, and summarization.
- New insights: LLMs can be used to analyze large datasets of text and code, which can reveal new insights about human language and behavior. This can be used to improve products and services or to develop new business models.
Overall, large language models are a powerful tool that can be used to improve a wide variety of tasks. As they continue to develop, we can expect to see even more innovative and exciting applications in the future.
Customizing Large Language Models
Fine-tuning and prompt engineering are two different approaches to using large language models (LLMs) for downstream tasks.
Fine-tuning involves training the LLM on a dataset of labeled examples for the specific task you want it to perform. This can be a time-consuming and data-intensive process, but it can result in high performance on the target task.
Prompt engineering involves providing the LLM with a prompt, which is a short piece of text that tells the model what to do. The prompt can be as simple as a few keywords, or it can be more complex, including examples of the desired output. Prompt engineering can be used to perform a variety of tasks, even if the LLM has not been specifically trained on those tasks.
Here is a table that summarizes the key differences between fine-tuning and prompt engineering:
Main challenges in the domain of LLM include:-
1). High Training Cost
2). Requirements of Large Datasets
3). Very Long Training Time
High Training Costs
- Training LLMs from scratch used to be very costly, requiring :-
GPT-4 is a multi-modal model that has 175 billion parameters, Sam Altman stated that the cost of training GPT-4 was more than $100 million.
- GPT-4 estimated training cost is upper bounded to $200M using 10,000 A100 X for 11 months. Accounting for trials and errors + research efforts might push it to $1B.
- Google BARD training cost can be similar
- Microsoft invested over 10 billion dollars in OpenAI in cash, and in terms of specially designed super computers for training LLMs
Training LLMs at Lower Cost
Several approaches are emerging to train the LLMs at lower cost :-
- Using high quality data e.g. Falcon model claims that they achieved reduction in training cost, and improved results using high quality data
- Fine-tuning existing model with instruction set e.g. alpaca_data.json contains 52K instruction-following data that was used for fine-tuning the Alpaca model
- Improvements in training algorithms such as LORA and QLORA is now allowing LLMs to be trained on low-cost consumer GPU’s
Alpaca Model(Instruction Following LLMs)
The Alpaca model is essentially a modified and fine-tuned version of the LLaMA model, designed to follow instructions similar to ChatGPT.
What makes the Alpaca model truly remarkable is that the entire fine-tuning process cost less than $600, whereas training the GPT-3 model in 2020 cost around $5,000,000.
Falcon 40B is the reigning open-source LLM on the Open LLM Leaderboard, with the 7B version being the best in its weight class. The driver behind Falcon’s performance lies in its training data, which unlike other LLMs is predominantly based on a novel large dataset called RefinedWeb.
Being trained on 1 trillion tokens, it required 384 GPUs on AWS, over two months. Trained on 1,000B tokens of RefinedWeb, a massive English web dataset built by TII.
Much less than OpenAI GPT-3 and GPT-4 models, but still millions of dollars.
Hallucinations in Large Language Models
Hallucinations in large language models (LLMs) are a phenomenon where the model generates text that is incorrect, nonsensical, or not real. This can happen for a variety of reasons, including:
- Limited contextual understanding: LLMs are trained on massive datasets of text, but they do not have the same understanding of the world as humans do. This can lead to them generating text that is not based on reality.
- Beam search: Beam search is a technique used to generate text from LLMs. It works by finding the most likely sequence of words that can be generated from the model’s current state. However, beam search can sometimes favor high-probability words that are not accurate or realistic.
- Input context: The input context that is given to an LLM can also influence the likelihood of hallucinations. For example, if an LLM is given a prompt that is about a fictional event, it may be more likely to generate text that is also fictional.
Impact of Size on the Performance of the Large Language Model
The size of a large language model (LLM) has a significant impact on its performance. In general, larger models tend to perform better than smaller models on a variety of tasks, such as natural language understanding, natural language generation, and machine translation. This is because larger models have more parameters, which allows them to learn more complex patterns in the data.
For example, a study by Radford et al. (2018) found that a 1.5 billion parameter model (GPT-2) outperformed a 117 million parameter model (GPT) on a variety of natural language understanding tasks. Additionally, a study by Brown et al. (2020) found that a 175 billion parameter model (GPT-3) outperformed a 117 million parameter model (GPT-2) on a variety of natural language generation tasks.
However, the impact of size on LLM performance is not always linear. For example, a study by Devlin et al. (2019) found that a 117 million parameter model (BERT) outperformed a 340 million parameter model (RoBERTa) on a variety of natural language understanding tasks. This suggests that there may be a point at which increasing the size of an LLM does not lead to a significant improvement in performance.
Overall, the impact of size on LLM performance is complex and depends on a variety of factors, such as the task at hand, the dataset used to train the model, and the architecture of the model. However, in general, larger models tend to perform better than smaller models on a variety of tasks.
Here are some additional factors that can affect the performance of an LLM:
- The quality of the training data
- The architecture of the model
- The optimization algorithm used to train the model
- The amount of computational resources available
As LLMs continue to grow in size, it is likely that their performance will continue to improve. However, it is also important to consider the limitations of LLMs, such as their potential for bias and their computational requirements.
In this article, we develop an understanding of LLMs, their applications, and challenges involved in designing/training LLMs and their applications. It is now well understood that large language models with more parameters are not always good, also we don’t need all the data for training these models. In many cases, a smaller model can outperform a larger model because it has better architecture, high quality data, and better instructions. Applications of Large Language models are not limited to generative AI but many large companies and organizations are looking at these models in terms of building their own AI systems that can act as intelligent advisors or assistants in a specific domain.