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Ritwik Gupta

@ritwikgupta.bsky.social

Assistant Professor at the University of Maryland, College Park | AI for Chaotic Environments and the Dual-Use Governance of AI

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Ritwik Gupta @ritwikgupta.bsky.social · 10/03/2025

This behavior has very interesting quirks. LLMs implicitly demonstrate time-discounting over the ICL examples. That is, recent evidence matters more!

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Ritwik Gupta @ritwikgupta.bsky.social · 10/03/2025

Interestingly, models follow a very similar trajectory to what the true Bayesian posterior should look like with the same amount of evidence! When we prompt for coin flips from a 60% heads-biased coin but give it evidence the follows 70% heads, models converge to the latter.

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Ritwik Gupta @ritwikgupta.bsky.social · 10/03/2025

Can we control this behavior? We tried many things before settling on in-context learning as a working mechanism. If we prompt an LLM to flip a biased coin, and then show increasing rollouts of flips from such a distribution, models converge to the right underlying parameter.

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Ritwik Gupta @ritwikgupta.bsky.social · 10/03/2025

Biased coin flips follow a simple probability distribution that LLMs should be able to simulate explicitly. In fact, when prompted to flip a fair coin, most LLMs predict heads 70-85% of the time! This holds true even if you prompt the model to flip a biased coin 🪙

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