According to Google Cloud, there are 3 main types of LLMs, each requiring a different type of prompt to make them effective in performing tasks.
Generic Language Model
-Example: GPT3 (Generative Pre-trained Transformer 3) by OpenAI
-Training Objective: Predict the next word in a sequence, aiming for broad understanding and generation of text.
-Training Data: Diverse sources like books, articles, and websites.
-Capabilities: Text completion, translation, summarization, question-answering.
-Use Cases: Versatile for any text generation or understanding task.
Instruction-Tuned Language Model
-Example: GPT3.5-turbo by OpenAI
-Training Objective: Specifically trained to follow instructions or respond to prompts for achieving specific goals.
-Training Data: Instructional texts and desired outputs, in addition to broad training.
-Capabilities: Better at understanding and executing specific instructions.
-Use Cases: Ideal for tasks requiring specific guidance, like data analysis or content creation with guidelines.
Dialog-Tuned Language Model
-Example: LaMDA (Language Model for Dialogue Applications) by Google
-Training Objective: Optimized for conversational understanding and generation.
-Training Data: Large dataset of conversational text focusing on dialogue nuances.
-Capabilities: Excels in maintaining context and coherence in conversations.
-Use Cases: Suitable for chatbots, virtual assistants, and customer service automation.
Notable
-GPT4 by OpenAI: A more advanced generic language model with enhanced capabilities in-context understanding, accuracy, and handling complex instructions. While not exclusively instruction-tuned or dialog-tuned, it performs well in these areas.
In summary:
-Generic Language Models like GPT3 and GPT4 by OpenAI are jack-of-all-trades, understanding and generating diverse text.
-Instruction-Tuned Models like GPT3.5-turbo by OpenAI excel in executing specific tasks outlined in instructions.
-Dialog-Tuned Models like LaMDA by Google are specialized for conversational contexts, ensuring coherence and relevance in dialogues.
Each model type is tailored to its specific application, showcasing the breadth and depth of modern language model capabilities.
More Examples:
Generic Language Model
GPT3 (Generative Pre-trained Transformer 3) by OpenAI: A highly versatile model capable of various text-based tasks.
BERT (Bidirectional Encoder Representations from Transformers) by Google: Primarily used for understanding the context of a word in search queries.
XLNet by Google/CMU: An autoregressive language model that outperforms BERT in some benchmarks.
Instruction-Tuned Language Model
GPT3.5-turbo by OpenAI: An enhanced version of GPT3, better at following specific instructions.
T5 (Text-To-Text Transfer Transformer) by Google: Designed to convert all NLP tasks into a text-to-text format, effectively understanding and executing instructions.
InstructGPT by OpenAI: A variant of the GPT model trained specifically to follow instructions and generate human-like text based on those instructions.
Dialog-Tuned Language Model
LaMDA (Language Model for Dialogue Applications) by Google: Specialized in generating conversational responses.
DialoGPT by Microsoft: A large-scale pretrained dialogue response generation model for conversational applications.
Meena by Google: A neural model trained for open-domain chatbot applications, focusing on maintaining context and coherence in conversations.
See Also: Large Language Models