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What is a LLM?

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A Large Language Model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massive data sets to understand and generate human-like responses to natural language queries
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LLMs are characterized by their large size, which can range from a billion to a trillion weights
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They are artificial neural networks that are pre-trained using self-supervised learning and semi-supervised learning
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LLMs can be used for various natural language processing (NLP) tasks, such as text generation, translation, content summary, rewriting content, classification, and categorization
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They can also be used as a foundation for customized use cases, and additional training on top of an LLM can create a finely tuned model for an organization's specific needs
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LLMs are used across industries for customer service purposes such as chatbots or conversational AI, marketing, legal, and banking
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They have the potential to disrupt content creation and the way people use search engines and virtual assistants
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However, LLMs can introduce unintended biases that can be introduced by LLM developers and self-supervised data collection from the internet
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What are some examples of LLMs?

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Here are some examples of Large Language Models (LLMs) based on the search results:
  • GPT (Generative Pretrained Transformer) by OpenAI
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  • BERT (Bidirectional Encoder Representations from Transformers) by Google
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  • RoBERTa by Facebook
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  • LaMDA by Google
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  • Turing NLG by Microsoft
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  • Gopher and Chinchilla by Deepmind
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  • Switch transformer, GLAM, PALM, Lamba, T5, MT5 by various organizations
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  • Claude v1, Text-ada-001, Text-babbage-001, Cohere, Falcon 40B by various organizations
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These LLMs have been used in various applications, including chatbots, language translation software, content creation tools, text summarization, question answering, sentiment analysis, speech recognition, and more.
What is GPT?

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GPT, which stands for Generative Pretrained Transformer, is a type of Large Language Model (LLM) and a prominent framework for generative artificial intelligence. It was first introduced in 2018 by OpenAI
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GPT is a transformer-based architecture and training procedure for natural language processing tasks
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It uses deep learning techniques and massive datasets to understand and generate human-like responses to natural language queries
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GPT models are characterized by their ability to generate text in response to an input sequence of words, also known as tokens. The output is a guess for the word most likely to be put at the end of the input sequence
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This simple input-output scheme is used to generate impressive dialogues, stories, and examples
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GPT models have been used in various applications, including chatbots, language translation software, content creation tools, text summarization, question answering, sentiment analysis, and more
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For example, ChatGPT, a chatbot developed by OpenAI, is a large language model-based chatbot that enables users to refine and steer a conversation towards a desired length
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GPT models have evolved over time, with newer versions offering improved capabilities. For instance, GPT-4, introduced in March 2023, accepts both text and images and is capable of handling over 25,000 words of text, allowing for use cases like long-form content creation, extended conversations, and document search and analysis
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Despite their impressive capabilities, GPT models also have limitations. For example, they can sometimes generate incorrect or nonsensical responses, and they can exhibit biases present in the data they were trained on
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What are some standard limitations of LLM's?

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Answer

Large Language Models (LLMs) have several standard limitations:
  1. Speed and Cost: LLMs are computationally expensive. Processing a single page of text requires computations across billions of parameters, which can result in high response times, especially for longer input documents. This computational complexity also comes with a financial cost, as LLMs generally require many dedicated GPUs and much more processing power than standard deep learning models
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  2. Unreliability: LLMs can be unreliable. Even if you ask an LLM the same question several times, they may give you a completely different answer each time
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    This unreliability makes it difficult to trust important processes to LLMs
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  3. Hallucinations: LLMs can generate information that wasn't in the training data, a phenomenon known as "hallucinations". This can lead to the generation of incorrect or nonsensical responses
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  4. Lack of Transparency: It can be challenging to understand why LLMs make certain predictions. This lack of transparency can make it difficult to trust and effectively use these models
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  5. Potential Bias: LLMs can exhibit biases present in the data they were trained on. These biases can lead to the generation of biased or discriminatory responses
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  6. Data Privacy: LLMs are trained on massive amounts of data, most of which is from the internet. This raises concerns about data privacy, especially if user input is used to continuously train and update the models
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  7. Limited Controllability: LLMs can sometimes generate outputs that are not desired or expected, indicating a lack of control over the model's responses
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  8. Stale Training Data: The training data used for LLMs can become outdated over time, which can affect the model's performance
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  9. Domain-Specific Knowledge: LLMs can struggle with domain-specific knowledge that is required for commercial use cases. Off-the-shelf LLMs are trained on enormous amounts of data across many domains, and therefore tend to perform poorly on domain-specific tasks
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  10. Data Exhaustion: Existing models require enormous training datasets, with the size increasing exponentially from one iteration to the next. High-quality language data is estimated to be exhausted by 2024 or 2025, and low-quality data by 2032
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These limitations highlight the challenges of using LLMs and the need for ongoing research to address these issues.

Related

What are some examples of hallucinations in LLMs?
How can LLMs be improved to address their limitations?
What are some potential risks associated with using LLMs?