LLMs have recently made impressive inroads on tasks whose output is
structured, such as coding, robotic planning and querying databases. The vision
of creating AI-powered personal assistants also involves creating structured
outputs, such as a plan for one's day, or for an overseas trip. Here, since the
plan is executed by a human, the output doesn't have to satisfy strict
syntactic constraints. A useful assistant should also be able to incorporate
vague constraints specified by the user in natural language. This makes LLMs an
attractive option for planning.
We consider the problem of planning one's day. We develop an LLM-based
planner (LLMPlan) extended with the ability to self-reflect on its output and a
symbolic planner (SymPlan) with the ability to translate text constraints into
a symbolic representation. Despite no formal specification of constraints, we
find that LLMPlan performs explicit constraint satisfaction akin to the
traditional symbolic planners on average (2% performance difference), while
retaining the reasoning of implicit requirements. Consequently, LLM-based
planners outperform their symbolic counterparts in user satisfaction (70.5% vs.
40.4%) during interactive evaluation with 40 users.

创建基于 LLMs 的规划器（LLMPlan）与能够将文本约束转化为符号表示的符号规划器（SymPlan），通过自我反思的能力以及对自然语言中模糊约束的合并，LLM 基于规划器在 40 名用户的互动评估中表现出优异的用户满意度（70.5%）且不输于传统符号规划器（40.4%）的显式约束满足率。

以人为中心的规划

Human-Centered Planning

Conversational agents are consistently growing in popularity and many people
interact with them every day. While many conversational agents act as personal
assistants, they can have many different goals. Some are task-oriented, such as
providing customer support for a bank or making a reservation. Others are
designed to be empathetic and to form emotional connections with the user. The
Alexa Prize Challenge aims to create a socialbot, which allows the user to
engage in coherent conversations, on a range of popular topics that will
interest the user. Here we describe Athena 2.0, UCSC's conversational agent for
Amazon's Socialbot Grand Challenge 4. Athena 2.0 utilizes a novel
knowledge-grounded discourse model that tracks the entity links that Athena
introduces into the dialogue, and uses them to constrain named-entity
recognition and linking, and coreference resolution. Athena 2.0 also relies on
a user model to personalize topic selection and other aspects of the
conversation to individual users.

UCSC's conversational agent, Athena 2.0, utilizes a novel knowledge-grounded discourse model for Amazon's Socialbot Grand Challenge 4, incorporating entity links, named-entity recognition, coreference resolution, and a user model for personalization.

Athena 2.0: 公开领域对话中的言论和用户建模

Athena 2.0: Discourse and User Modeling in Open Domain Dialogue

Task-oriented personal assistants enable people to interact with a host of
devices and services using natural language. One of the challenges of making
neural dialogue systems available to more users is the lack of training data
for all but a few languages. Zero-shot methods try to solve this issue by
acquiring task knowledge in a high-resource language such as English with the
aim of transferring it to the low-resource language(s). To this end, we
introduce CrossAligner, the principal method of a variety of effective
approaches for zero-shot cross-lingual transfer based on learning alignment
from unlabelled parallel data. We present a quantitative analysis of individual
methods as well as their weighted combinations, several of which exceed
state-of-the-art (SOTA) scores as evaluated across nine languages, fifteen test
sets and three benchmark multilingual datasets. A detailed qualitative error
analysis of the best methods shows that our fine-tuned language models can
zero-shot transfer the task knowledge better than anticipated.

CrossAligner 是一种实现零 - shot 跨语言传递任务知识的有效方法，利用未标记并行数据进行学习对齐，其细调语言模型优于预期。

CrossAligner & Co: 面向任务的跨语言自然语言理解的零 - shot 传递方法

CrossAligner & Co: Zero-Shot Transfer Methods for Task-Oriented Cross-lingual Natural Language Understanding

Recent years have seen significant market penetration for voice-based
personal assistants such as Apple's Siri. However, despite this success, user
take-up is frustratingly low. This position paper argues that there is a
habitability gap caused by the inevitable mismatch between the capabilities and
expectations of human users and the features and benefits provided by
contemporary technology. Suggestions are made as to how such problems might be
mitigated, but a more worrisome question emerges: "is spoken language
all-or-nothing"? The answer, based on contemporary views on the special nature
of (spoken) language, is that there may indeed be a fundamental limit to the
interaction that can take place between mismatched interlocutors (such as
humans and machines). However, it is concluded that interactions between native
and non-native speakers, or between adults and children, or even between humans
and dogs, might provide critical inspiration for the design of future
speech-based human-machine interaction.

声控助手的普及是受欢迎的，但由于人的能力与期望与现有技术的功能和好处之间不可避免的不匹配，存在可居住差距，拟解决此类问题，但是否存在语言交互的固有限制？基于目前对语言的特殊性质的看法，回答是可能存在。然而，作者得出结论，将来的人机交互设计可以从母语与非母语发言者、成人和儿童以及人类和狗之间的交互中获得关键的灵感。