Implicit Statistical Inference in Transformers: Approximating Likelihood-Ratio Tests In-Context
In-context learning (ICL) allows Transformers to adapt to novel tasks without weight updates, yet the underlying algorithms remain poorly understood. We adopt a statistical decision-theoretic perspective by investigating simple binary hypothesis testing, where the optimal policy is determined by the likelihood-ratio test. Notably, this setup provides a mathematically rigorous setting for mechanistic interpretability where the target algorithmic ground truth is known. By training Transformers on
Record details
Published: 11 March 2026
Source: arXiv
Category: Research
Topics: Regulation · Safety & alignment · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (11 March 2026), “Implicit Statistical Inference in Transformers: Approximating Likelihood-Ratio Tests In-Context,” evidence record 7387, https://ethics.ai/record/7387 (originally published by arXiv).
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