gsarti
Abstract
This position paper argues that, in order to understand AI, we cannot rely on our existing vocabulary of human words. Instead, we should strive to develop neologisms: new words that represent precise human concepts that we want to teach machines, or machine concepts that we need to learn. We start from the premise that humans and machines have differing concepts. This means interpretability can be framed as a communication problem: humans must be able to reference and control machine concepts, and communicate human concepts to machines. Creating a shared human-machine language through developing neologisms, we believe, could solve this communication problem. Successful neologisms achieve a useful amount of abstraction: not too detailed, so they're reusable in many contexts, and not too high-level, so they convey precise information. As a proof of concept, we demonstrate how a "length neologism" enables controlling LLM response length, while a "diversity neologism" allows sampling more variable responses. Taken together, we argue that we cannot understand AI using our existing vocabulary, and expanding it through neologisms creates opportunities for both controlling and understanding machines better.
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Thanks, very interesting paper
But each language is formed by everyday life necessity. Many African tribes did not had a word for snow, once Eskimo people have dozens of words for different type of snow .
If there is no need for new word in everyday human communication - any neologism is a dead from birth. For sure there will be some new verbs like "let's google/shazam/etc it" but what's inside model - ordinary people have no any need in it, it's like nobody know names of Boeing internal details - they just flying by it, and process definitions are limited to type of seat, meal, airport, schedule, etc
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Exactly what I thought. Still remember words like protolangium (when LM only starts pre-training) and cognitract (neural network as a continuous cognitive organ) from my own attempts to conjure up new terminology with AI as co-authors. I hypothesised that LLMs might become smarter if using more precise wording that is not needed for humans, but it requires more quality data on words (like wiki) for the LM to effectively use the new words. Anyway, still a question what exact words are needed both by humans and AI to cover any gaps. Maybe there could be a simplified pipeline of creating new language additions, but it needs proper direction.
Created r/AIneologisms, citing the paper.
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