**Links to**: [[000 Question]], [[Semantic attractor]], [[NLP]], [[Semantics]], [[Syntax]], [[Linguistics]], [[NLU]], [[Entropy]], [[Noise]], [[Probability]], [[Vector]], [[Vector space model]], [[Calculation]], [[Neural nets]], [[Evolution]], [[Degeneracy]], [[Redundancy]], [[Kripkenstein]], [[002.1 Modulations]]. # ❝𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐦𝐨𝐝𝐞𝐥𝐬❞ Language models are probabilistic string-organizers. They only _learn_ when they get updated. They are notoriously anti-generative because much of their inner logic is structured around the reduction of entropy. What astonished the world when they first rolled out into the public, was a simple chatbot interface. Indeed, I often say “please” to them “thank you” to them. >“If reasoning problems could be solved with nothing more than a single step of deductive inference, then an LLM’s ability to answer questions such as this might be sufficient. But non-trivial reasoning problems require multiple inference steps. LLMs can be effectively applied to multi-step reasoning, without further training, thanks to clever prompt engineering. In chain-of-thought prompting, for example, a prompt prefix is submitted to the model, before the user’s query, containing a few examples of multi-step reasoning, with all the intermediate steps explicitly spelled out (Nye et al., 2021; Wei et al., 2022). Including a prompt prefix in the chain-of-thought style encourages the model to generate follow-on sequences in the same style, which is to say comprising a series of explicit reasoning steps that lead to the final answer. As usual, the question really being posed to the model is of the form “Given the statistical distribution of words in the public corpus, what words are likely to follow the sequence S”, where in this case the sequence S is the chain-of-thought prompt prefix plus the user’s query. The sequences of tokens that are most likely to follow S will have a similar form to sequences found in the prompt prefix, which is to say they will include multiple steps of reasoning, so these are what the model generates.” > >Murray Shanahan, “Talking about large language models”, 2023, p. 8. See: [[002 Semantic noise]] for some thoughts on LLMs. ![[kollektiv 1973.png|600]] <small>Fig. 1. Album cover detail, Kollektiv, self-titled, 1973.</small>