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.

我们对基于检索增强的大型语言模型在长篇问题回答中的接地性进行了实证研究。研究发现，尽管生成的句子中包含正确的答案，但在 3 个数据集和 4 个模型系列中，存在大量一致性未接地的句子。此外，我们还研究了模型大小、解码策略和指导调优等因素对接地性的影响。结果显示，尽管较大的模型更有效地将输出接地，但仍有相当数量的正确答案受到虚构内容的影响。这项研究为长篇问题回答中的接地性挑战提供了新的见解，并强调了在大型语言模型中采取更鲁棒的机制来减少未接地内容的生成的必要性。

检索增强的长文生成中的基础性：一项实证研究

Groundedness in Retrieval-augmented Long-form Generation: An Empirical  Study

Many complex problems are naturally understood in terms of symbolic concepts.
For example, our concept of "cat" is related to our concepts of "ears" and
"whiskers" in a non-arbitrary way. Fodor (1998) proposes one theory of
concepts, which emphasizes symbolic representations related via constituency
structures. Whether neural networks are consistent with such a theory is open
for debate. We propose unit tests for evaluating whether a system's behavior is
consistent with several key aspects of Fodor's criteria. Using a simple visual
concept learning task, we evaluate several modern neural architectures against
this specification. We find that models succeed on tests of groundedness,
modularlity, and reusability of concepts, but that important questions about
causality remain open. Resolving these will require new methods for analyzing
models' internal states.

本研究提出了关于符号概念的理论，并使用一个简单的视觉概念学习任务来评估多个现代神经结构与该理论的一致程度，发现这些模型可以成功通过测验，但在因果关系方面仍然存在重要的问题。