  We present an empirical study of groundedness in long-form question answering
(LFQA) by retrieval-augmented large language models (LLMs). In particular, we
evaluate whether every generated sentence is grounded in the retrieved
documents or the model's pre-training data. Across 3 datasets and 4 model
families, our findings reveal that a significant fraction of generated
sentences are consistently ungrounded, even when those sentences contain
correct ground-truth answers. Additionally, we examine the impacts of factors
such as model size, decoding strategy, and instruction tuning on groundedness.
Our results show that while larger models tend to ground their outputs more
effectively, a significant portion of correct answers remains compromised by
hallucinations. This study provides novel insights into the groundedness
challenges in LFQA and underscores the necessity for more robust mechanisms in
LLMs to mitigate the generation of ungrounded content.
