Many real-world applications require surfacing extracted snippets to users,
whether motivated by assistive tools for literature surveys or document
cross-referencing, or needs to mitigate and recover from model generated
inaccuracies., Yet, these passages can be difficult to consume when divorced
from their original document context. In this work, we explore the limits of
LLMs to perform decontextualization of document snippets in user-facing
scenarios, focusing on two real-world settings - question answering and
citation context previews for scientific documents. We propose a
question-answering framework for decontextualization that allows for better
handling of user information needs and preferences when determining the scope
of rewriting. We present results showing state-of-the-art LLMs under our
framework remain competitive with end-to-end approaches. We also explore
incorporating user preferences into the system, finding our framework allows
for controllability.

本文研究了基于 LLMs 的篇章去文脉化在用户面向场景下的局限性，主要集中在两个现实世界应用中，即问答和科学文献的引文预览。我们提出了一个针对篇章去脉冲化的问答框架，该框架允许更好地处理用户信息需求和偏好，我们的结果表明，在我们的框架下，最先进的 LLMs 仍然具有与端到端方法相当的竞争力。我们还探索了将用户偏好纳入系统中，发现我们的框架可以实现可控性。