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Evidence record 6772 · automatically gathered

The Alignment Tax: Response Homogenization in Aligned LLMs and Its Implications for Uncertainty Estimation

RLHF-aligned language models exhibit response homogenization: on TruthfulQA (n=790), 40-79% of questions produce a single semantic cluster across 10 i.i.d. samples. On affected questions, sampling-based uncertainty methods have zero discriminative power (AUROC=0.500), while free token entropy retains signal (0.603). This alignment tax is task-dependent: on GSM8K (n=500), token entropy achieves 0.724 (Cohen's d=0.81). A base-vs-instruct ablation confirms the causal role of alignment: the base mod

Record details

Published: 25 March 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Safety & alignment

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How to cite this record

ethics.ai (25 March 2026), “The Alignment Tax: Response Homogenization in Aligned LLMs and Its Implications for Uncertainty Estimation,” evidence record 6772, https://ethics.ai/record/6772 (originally published by arXiv).

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