Users of natural language interfaces, generally powered by Large Language
Models (LLMs),often must repeat their preferences each time they make a similar
request. To alleviate this, we propose including some of a user's preferences
and instructions in natural language -- collectively termed standing
instructions -- as additional context for such interfaces. For example, when a
user states I'm hungry, their previously expressed preference for Persian food
will be automatically added to the LLM prompt, so as to influence the search
for relevant restaurants. We develop NLSI, a language-to-program dataset
consisting of over 2.4K dialogues spanning 17 domains, where each dialogue is
paired with a user profile (a set of users specific standing instructions) and
corresponding structured representations (API calls). A key challenge in NLSI
is to identify which subset of the standing instructions is applicable to a
given dialogue. NLSI contains diverse phenomena, from simple preferences to
interdependent instructions such as triggering a hotel search whenever the user
is booking tickets to an event. We conduct experiments on NLSI using prompting
with large language models and various retrieval approaches, achieving a
maximum of 44.7% exact match on API prediction. Our results demonstrate the
challenges in identifying the relevant standing instructions and their
interpretation into API calls.

包含自然语言界面中用户的偏好与指示（称为固定指令）作为额外上下文的提案，通过为 NLSI 构建一个含有用户对话与特定固定指令的数据集，发展了包含约 2.4K 对话与 17 个领域的语言到程序转换数据集，其中的关键挑战之一是确定哪些固定指令适用于给定对话，并对其进行 API 调用的解释。