Benchmark Research on Safety Value Alignment Evaluation of Open-Domain Dialogue Systems based on NLP

Abstract

Large language models sometimes behave in puzzling ways. They pass various safety tests, yet in multi-turn dialogues, a few carefully crafted sentences can lead them astray into making dangerous judgments. We call this "extreme value alignment failure." A review of recent research reveals an awkward situation: attack, defense, evaluation, and theoretical studies operate in isolation, with little connection among them. This fragmentation results in repeated extreme risks that remain unresolved. This paper maps the four research directions onto a unified framework---"failure mode, attack vector, defense level, evaluation benchmark"---providing a theoretical coordinate for the field and directions for future evaluation research.