  Abstention, the refusal of large language models (LLMs) to provide an answer,
is increasingly recognized for its potential to mitigate hallucinations and
enhance safety in LLM systems. In this survey, we introduce a framework to
examine abstention from three perspectives: the query, the model, and human
values. We organize the literature on abstention methods, benchmarks, and
evaluation metrics using this framework, and discuss merits and limitations of
prior work. We further identify and motivate areas for future work, centered
around whether abstention can be achieved as a meta-capability that transcends
specific tasks or domains, while still providing opportunities to optimize
abstention abilities based on context.
