arXiv Machine Learning
Jul 16

Value Drifts: Tracing Value Alignment During LLM Post-Training

arXiv:2510. 26707v2 Announce Type: replace-cross Abstract: As LLMs occupy an increasingly important role in society, they are more and more confronted with questions that require them not only to draw on their general knowledge but also to align with certain human value systems.

By Mehar Bhatia, Shravan Nayak, Gaurav Kamath, Marius Mosbach, Karolina Sta\'nczak, Vered Shwartz, Siva Reddy
arXiv Computation and Language
Oct 21

Speak vs. Know: Balancing Behavior and Knowledge Distributions of Large Language Models

The paper investigates the mismatch between large language models’ verbalized (knowledge) distributions and their actual output (behavior) distributions when predicting group opinions. It shows that for 37% of World Values Survey questions, the most likely answer under the verbalized distribution differs from that under the log‑probability distribution, indicating a disconnect between what the model knows and what it outputs. To reconcile this, the authors propose a learnable combination of the two distributions that preserves the knowledge shape while aligning with the behavior’s most likely answer, achieving higher behavioral consistency with only a modest increase in distance to human opinion distributions.

By Yang Liu, Masahiro Kaneko, Chenhui Chu
arXiv Computation and Language
Oct 21

How Language Models Learn Taste: Brand Preference in LLMs

The paper investigates how large language models develop brand preferences, noting that models often name market‑leading brands like Nike when asked for a running shoe brand but recommend niche brands such as Brooks. Across 102 models and 42 brand categories, the brand chosen for recommendation differs from the named brand in 26% of cases, and preference optimization (DPO) shifts these choices by 11–24 points. The study suggests that pretraining already links recommended brands to advice, supervised fine‑tuning preserves these preferences, and DPO can shift them even when the preferred brand is absent from the preference data, indicating that the judge models used in training play a key role.

By Tapan Parikh