QCards AI

CCoT is a prompt-engineering technique aimed at reducing LLM response verbosity & inference time. It reduces response length by 48.70% for multiple-choice Q&A, with unchanged problem-solving performance. For math problems, it incurs a 27.69% performance penalty, but leads to an average token cost reduction of 22.67%.

RPT6 is part of the proteasome complex in the hippocampus and also regulates gene expression during memory formation.

LLMs are used for generating plausible text responses, summarization, question answering, text classification, solving math problems, writing code, mimicking human speech patterns, combining information with different styles and tones. They are also used to build sentiment detectors, toxicity classifiers, and generate image captions.

Self-attention is a key concept in transformers where each token focuses on its relationship with other tokens. It asks ‘How much does every other token of input matter to me?’ For example, in a sentence, each word calculates the relevance of other words to understand pronouns or ambiguous references.

Transformers are an architecture designed around the idea of attention and are used for processing longer sequences in language modeling. They consist of an encoder and a decoder, where the encoder converts input text into an intermediate representation and the decoder converts that representation into useful text.

Large language models are trained using a large corpus of high-quality data. During training, the model iteratively adjusts parameter values through self-learning techniques, maximizing the likelihood of the next tokens in the training examples. Once trained, they can be adapted to perform multiple tasks using fine-tuning, zero-shot learning, and few-shot learning.

Large language models use multi-dimensional vectors, known as word embeddings, to represent words so that words with similar contextual meanings or other relationships are close in the vector space. Transformers then pre-process text using word embeddings to understand word and phrase context, parts of speech, and other relationships, before producing unique outputs through the decoder.