Polymarket and other prediction markets attribute relative truth to statements (0-100%). LLMs take a term (or sequence of terms) and try to determine the maximum probability for which term or sequence of terms follows on that. If you think of prediction market statements as tokens to which the market is supposed to find the next optimal and final token, then it becomes clear that both concepts are rather similar in their optimization method. This includes the finite nature of their output, as well as that both work with correlations instead of causality. (Yes, prediction market equilibriums also have the character of correlations, because the aggregate of all bets loses the assumed causal decision process by individual market participants.) The two concepts only differ in three main aspects: 1) The degree of “truth” of their final verdict. LLMs seek relative “truth”; prediction markets seek absolute truth. 2) The pool of information from which they derive their conclusion. Prediction markets use free floating information and speculation; LLMs use more or less fixed training material. 3) The way of internalizing information for their prediction. Prediction markets balance the quantity of scarce tokens; LLMs compute (theoretically) non-scarce tokens of various sizes. Thanks to the similarities of the two concepts these particular differences could be utilized to critically improve the quality of LLM output. Imagine every token is put on a prediction market for verification, where users (and other LLMs) can give their opinion. I think such a probabilistic 2nd layer based on scarcity would free LLMs from their limitation to training material and its inherent bias. Granted, the 2nd layer would have its own inherent bias, but this bias is for one only temporary and therefore dynamic and as such self-improving, which is what you want. And this 2nd layer also contains its own quality given the money involved in the bets, assuming the prediction market participants individually have made a qualitative analysis before making their bet. The overall result would be that every element of all LLM output tokens are weighted by the risk the market perceives to be justified. Now imagine thousands of local LLMs engaging on prediction markets and offering on the one side their valuable private information for money (by betting) and receiving back on the other side qualitative assessments on their own tokens they put up to be “measured” with bets. I think this would propel both LLMs and prediction markets to a new level. The current insider information game some high profile individuals play especially on Polymarket would become a small side business. LLMs could offer bets at an incredibly faster rate and also bet on much more and more detailed statements than is possible with human actors on the market. Is this feasible, or is maybe someone already building this? I imagine this may have the chance to be the next big thing in the area. [Please note: I’m not a pro in the business, just an interested observer on the topic.] submitted by /u/Extrogrl
Originally posted by u/Extrogrl on r/ArtificialInteligence
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