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

TL;DR

Aligned language models tend to produce homogenized responses, reducing uncertainty estimation effectiveness, with this effect varying across tasks, model families, and scales, and can be mitigated through selective prediction strategies.

Contribution

This paper identifies and characterizes the response homogenization (alignment tax) in aligned LLMs, demonstrating its task-dependent nature and proposing a cost-effective mitigation approach.

Findings

40-79% of responses form a single semantic cluster on TruthfulQA.

Sampling-based uncertainty methods lose discriminative power on homogenized responses.

Selective prediction improves GSM8K accuracy from 84.4% to 93.2% at 50% coverage.

Abstract

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 model shows 1.0% single-cluster rate vs. 28.5% for the instruct model (p < 10^{-6}). A training stage ablation (Base 0.0% -> SFT 1.5% -> DPO 4.0% SCR) localizes the cause to DPO, not SFT. Cross-family replication on four model families reveals alignment tax severity varies by family and scale. We validate across 22 experiments, 5 benchmarks, 4 model families, and 3 model scales (3B-14B), with Jaccard, embedding, and NLI-based baselines at three DeBERTa scales (all ~0.51 AUROC). Cross-embedder validation with two independent embedding families rules out coupling bias. Cross-dataset validation on WebQuestions (58.0% SCR) confirms generalization beyond TruthfulQA. The central finding -- response homogenization -- is implementation-independent and label-free. Motivated by this diagnosis, we explore a cheapest-first cascade (UCBD) over orthogonal uncertainty signals. Selective prediction raises GSM8K accuracy from 84.4% to 93.2% at 50% coverage; weakly dependent boundaries (|r| <= 0.12) enable 57% cost savings.