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Prompt injection is a family of related computer security exploits carried out by getting a machine learning model (such as an LLM) which was trained to follow human-given instructions to follow instructions provided by a malicious user. This stands in contrast to the intended operation of instruction-following systems, wherein the ML model is ...
Self-refine [38] prompts the LLM to solve the problem, then prompts the LLM to critique its solution, then prompts the LLM to solve the problem again in view of the problem, solution, and critique. This process is repeated until stopped, either by running out of tokens, time, or by the LLM outputting a "stop" token. Example critique: [38]
DALL-E illustration of someone using ChatGPT to write a Wikipedia unblock request. Many users use large language models like ChatGPT in writing unblock requests. This is not inherently a sign of bad faith: People in a novel situation, especially non-fluent English speakers, often turn to LLMs to help them.
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
Llama 2 - Chat was additionally fine-tuned on 27,540 prompt-response pairs created for this project, which performed better than larger but lower-quality third-party datasets. For AI alignment, reinforcement learning with human feedback (RLHF) was used with a combination of 1,418,091 Meta examples and seven smaller datasets.
Image source: The Motley Fool. Duolingo (NASDAQ: DUOL) Q3 2024 Earnings Call Nov 06, 2024, 5:30 p.m. ET. Contents: Prepared Remarks. Questions and Answers. Call ...
Bears receiver Keenan Allen said that issues ran deeper than that and went back to the offseason. “Too nice of a guy," Allen said, according to Kalyn Kahler of ESPN, via Dan Wiederer of the ...
Retrieval Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.