What is Large Language Model (LLM)
Large Language Models (LLMs) represent a subset of language models crafted using colossal datasets through deep learning techniques. The ability of LLMs to facilitate conversations resembling human interactions and their advanced proficiency in natural language processing has garnered worldwide recognition.
In the context of LLMs, the term "large" pertains to a substantial expansion in three key elements: computational capacity, data volume, and the quantity of parameters, in contrast to traditional language models. "Computational resources" imply the processing power of a computer. "Data volume" points to the quantity of text data fed into a computer. "Model parameters" denote the intricacy of parameters specific to deep learning technologies, forming an array of coefficients for probabilistic calculations. LLMs have witnessed rapid development by expanding these three aspects, as detailed in OpenAI's 2020 paper. This paper suggests a correlation between the performance of language models and these three factors. Leveraging these insights, OpenAI has successfully developed highly accurate LLMs by substantially augmenting these three elements. ChatGPT, unveiled in November 2022, is a notable example of an LLM, enhancing the quality of natural language responses with its superior replies.
Types of LLMs
As of 2023, various LLMs have been announced.
| Model Name | Summary | Company | Parameter Count | Release Date |
|---|---|---|---|---|
| GPT-3 | A model tuned for document generation based on the Generative Transformer. | OpenAI | 175 billion | May 2020 |
| GPT-4 | A model that learns multimodal data (such as images and audio) in addition to text in GPT-3. | OpenAI | Over 200 billion | March 2023 |
| LaMDA | A model based on the Transformer, tuned for conversations. | Not disclosed | May 2021 | |
| PaLM | It improved performance by significantly increasing the number of parameters based on the Transformer. | 540 billion | April 2022 | |
| LLaMA | Demonstrates performance equivalent to GPT-3 with significantly fewer parameters than GPT-3. Lightweight and operable on a single GPU. | Meta | 70 to 650 billion | February 2023 |
| Alpaca 7B | Fine-tuned using results of Instruction-following (generating its own learning data) based on LLaMA. | Stanford University | 70 billion | March 2023 |
What LLMs can do
LLMs are trained on text data and excel in the following text processing tasks.
| Task | Description |
|---|---|
| Machine Translation | Generates natural translations from one language to another. |
| Summarization | Condenses long texts. |
| Question Answering | Answers questions about a text in natural language. |
| Text Generation | Generates lengthy texts according to a theme. |
| Sentiment Analysis | Analyzes the tone and emotion of a text. |
| Language Generation Tasks | Generates various types of texts such as descriptions, news articles, novels, poems, advertisements. |
| Keyword Extraction | Extracts important keywords from a text. |
| Word Embedding | Converts words into numerical vectors used in other natural language processing tasks. |
| Text Classification | Classifies text documents and labels them. |
| Text Paraphrasing | Generates more natural expressions while maintaining the same meaning by translating text into different expressions. |
Challenges with LLMs
Despite their remarkable abilities, large language models also grapple with several obstacles. They are prone to produce incorrect data or hallucinations. Additionally, there's the potential hazard of prompt injection, where harmful prompts are manipulated to activate forbidden functionalities and elicit inappropriate replies. Moreover, as LLMs process information up to a certain point and provide responses based on it, they might disseminate outdated data. Concurrently with endeavors to boost performance, research is underway to address these challenges.
References
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