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.

翻译：我们通过实证研究探讨了检索增强的大型语言模型在长文本问答（LFQA）中的溯源性。特别地，我们评估了每个生成的句子是否源于检索到的文档或模型的预训练数据。在3个数据集和4个模型系列中，我们的发现揭示出，即使这些句子包含正确的地面真实答案，仍有相当一部分生成的句子始终缺乏溯源性。此外，我们考察了模型大小、解码策略和指令微调等因素对溯源性的影响。研究结果表明，尽管较大的模型倾向于更有效地将其输出溯源，但正确答案的很大一部分仍然受到幻觉的影响。本研究为LFQA中的溯源性挑战提供了新颖的见解，并强调了在大型语言模型中建立更稳健机制以减轻无溯源内容生成的必要性。