  As Large Language Models (LLMs) achieve remarkable performance across various
NLP tasks, their reliability becomes essential for widespread adoption. This
paper focuses on Abstention Ability (AA), a critical yet under explored aspect
of reliability - the ability of LLMs to refrain from answering questions when
they are uncertain or when definitive answer is not possible, while maintaining
question-answering (QA) task performance. While previous works have focused on
understanding the recollection abilities of LLMs or their ability to identify
imponderable/unanswerable questions, we believe there is a need for an
effective AA evaluation method. Therefore, we propose a black-box evaluation
methodology to examine and understand the AA of LLMs across a variety of
multiple-choice QA tasks. We measure AA by rewarding models for abstaining from
answering when their predictions are incorrect or when the questions are
inherently unanswerable. We investigate three strategies, Strict Prompting,
Verbal Confidence Thresholding, and Chain-of-Thought (CoT), to understand their
impact on abstention across different LLMs. Our findings reveal that while even
state-of-the-art LLMs like GPT-4 struggle with abstention, strategic prompting
such as CoT, can significantly enhance this ability. Furthermore, we
demonstrate that improving AA also leads to better overall QA task performance,
underscoring the importance of evaluating AA in LLMs.
