LLMs are powered by deep neural networks and leverage transformer architectures to process and generate language. Trained on vast datasets encompassing books, articles, and websites, they learn to predict and produce text based on patterns and contexts. Once you understand the underlying logic of LLMs, the rest of the arguments will be obvious. Mathematically, they operate by maximising the [[probability]] of a sequence: _P_(Sequence)=∏{_t_=1}/_N_(_wt|w_1,_w_2,…,_wt_−1) where _wt_ is the word at position _t_, and _N_ is the length of the sequence. **Breaking It Down in Simple Terms:** - **_P_****(Sequence):** This represents the overall probability of a particular sequence of words (a sentence or paragraph). - **∏{****_t_****=1}/****_N_****:** The capital Pi symbol (∏) stands for "product," which means we multiply a series of terms together. Here, we multiply terms from _t_=1 to _t_=_N_, where _N_ is the total number of words in the sequence. - **_P_****(****_wt_****∣****_w_****1,****_w_****2,…,****_wt_****−1):** This is the probability of the word at position _t_ (_wt_) occurring, given all the previous words in the sequence (_w_1,_w_2,…,_wt_−1). **Simplifying the Concept:** 1. **Word Prediction One at a Time:** The model starts at the first word and predicts the next word based on what it has seen so far. For example, after "The dog" it might predict "ate" because "The dog ate..." is a statistically common phrase. 2. **Calculating Probabilities:** The model calculates the likelihood of each possible next word at each position in the sequence. It uses patterns learned from training data to make these predictions. 3. **Building Up the Sequence:** The probabilities of each predicted word are multiplied together to find the overall probability of the entire sequence. The model aims to choose words that make the sequence as probable (natural and logical) as possible. **An Everyday Example:** Imagine you're trying to guess the next word in a sentence based on what someone has already said: - **Given Words:** "Once upon a" - **Possible Next Words:** "time," "tree," "clock," etc. - **Most Probable Next Word:** "time" (because "once upon a time" is the most likely next word statistically). The model calculates that "time" has the highest probability given the previous words, so it selects "time" as the next word. To put it plainly, Large Language Models (LLMs) like GPT-4 generate text by predicting one word at a time based on the words that have come before. They aim to produce the most likely sequence of words according to patterns they've learned from vast amounts of data. The strengths of LLMs, therefore, are: - **Language Fluency**: Generating coherent and contextually appropriate responses. - **Knowledge Recall**: Retrieving information absorbed during training. - **Adaptability**: Handling a wide range of topics and styles. However, they are architecturally determined statistically instead of understanding the meaning of logic or originating, let alone originating with the agency as hyped by many.