Hallucination
Hallucination in AI refers to when a model generates plausible-sounding but factually incorrect or fabricated information. Reducing hallucinations is a major challenge for large language models used in high-stakes applications.
Understanding Hallucination
Hallucination occurs when an AI model generates content that appears plausible but is factually incorrect or entirely fabricated. This is one of the most persistent challenges facing large language models, which can confidently produce fake citations, invent statistics, or describe events that never happened. Hallucinations arise because language models are trained to predict likely text sequences rather than verify truth. Techniques like grounding, retrieval-augmented generation, and chain-of-thought prompting help reduce hallucination rates. In high-stakes domains such as healthcare, law, and finance, hallucinations can have serious consequences, making detection and mitigation a top priority. Benchmarking hallucination rates has become a standard part of model evaluation, and researchers continue developing methods to improve factual consistency in AI-generated text.
Category
Generative AI
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Chain of Thought
Chain of thought is a prompting technique that encourages large language models to break down complex reasoning into intermediate steps. This approach significantly improves performance on math, logic, and multi-step reasoning tasks.
ChatGPT
ChatGPT is an AI chatbot developed by OpenAI that uses large language models to generate human-like conversational responses. It became one of the fastest-growing consumer applications in history after its launch in November 2022.
Claude
Claude is an AI assistant developed by Anthropic, designed to be helpful, harmless, and honest. It is built using Constitutional AI techniques and competes with models like GPT-4 and Gemini.
Diffusion Model
A diffusion model is a generative AI model that creates data by learning to reverse a gradual noise-adding process. Diffusion models power state-of-the-art image generation systems like Stable Diffusion and DALL-E.
Discriminator
A discriminator is the component of a GAN that learns to distinguish between real and generated data. It provides feedback to the generator, creating an adversarial training dynamic that improves output quality.
Few-Shot Prompting
Few-shot prompting provides a language model with a small number of input-output examples in the prompt to demonstrate the desired task format. This technique helps models understand task requirements without any fine-tuning.
Foundation Model
A foundation model is a large AI model trained on broad data that can be adapted to a wide range of downstream tasks. GPT-4, Claude, Gemini, and DALL-E are examples of foundation models that serve as bases for specialized applications.
GAN
A GAN (Generative Adversarial Network) is a generative model consisting of two competing neural networks — a generator and a discriminator. GANs produce realistic synthetic data by training these networks in an adversarial game.