most citedGeopolitical biases in LLMs: what are the "good" and the "bad" countries according to contemporary language models

2 citations · 2 across the 1 of their papers we have counts for

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2 papers

cs.CL2025★ 2 cited

Geopolitical biases in LLMs: what are the "good" and the "bad" countries according to contemporary language models

Mikhail Salnikov, Dmitrii Korzh, Ivan Lazichny +7

This paper evaluates geopolitical biases in LLMs with respect to various countries though an analysis of their interpretation of historical events with conflicting national perspec…

cs.CL2025

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models

Artem Vazhentsev, Lyudmila Rvanova, Ivan Lazichny +4

Uncertainty quantification (UQ) is a prominent approach for eliciting truthful answers from large language models (LLMs). To date, information-based and consistency-based UQ have b…