  Large audio-language models (LALMs) enhance traditional large language models
by integrating audio perception capabilities, allowing them to tackle
audio-related tasks. Previous research has primarily focused on assessing the
performance of LALMs across various tasks, yet overlooking their reliability,
particularly concerning issues like object hallucination. In our study, we
introduce methods to assess the extent of object hallucination of publicly
available LALMs. Our findings reveal that LALMs are comparable to specialized
audio captioning models in their understanding of audio content, but struggle
to answer discriminative questions, specifically those requiring the
identification of the presence of particular object sounds within an audio
clip. This limitation highlights a critical weakness in current LALMs: their
inadequate understanding of discriminative queries. Moreover, we explore the
potential of prompt engineering to enhance LALMs' performance on discriminative
questions.
