AI BriefingConversational AI Agent Technology and Model Comparison
Key point
This analyzes and compares the technical characteristics and training methodologies of major conversational AI models, including ChatGPT.
Details
We examine the technologies behind ChatGPT's success, such as RLHF, IFT, CoT, and Red teaming, and analyze the technical landscape of major conversational AI agents.
Comparison of Major Conversational Agents We compare Google's LaMDA, Meta's BlenderBot 3, DeepMind's Sparrow, OpenAI's ChatGPT(InstructGPT), and Anthropic's Assistant based on model size, training data, accessibility, and evaluation criteria. The common goal across all models is Instruction Following ability, i.e., the capacity to follow user instructions.
Instruction Fine-Tuning (IFT) Beyond the base language model's simple text prediction ability, IFT is used as a core method to accurately carry out the user's intent. IFT trains the model using data composed of sets of Instruction, Input, and Output for various tasks. This data leverages content either written directly by humans or generated (Bootstrapping) via language models.
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