Programming LLM-based knowledge and task assistants that faithfully conform
to developer-provided policies is challenging. These agents must retrieve and
provide consistent, accurate, and relevant information to address user's
queries and needs. Yet such agents generate unfounded responses
("hallucinate"). Traditional dialogue trees can only handle a limited number of
conversation flows, making them inherently brittle. To this end, we present
KITA - a programmable framework for creating task-oriented conversational
agents that are designed to handle complex user interactions. Unlike LLMs, KITA
provides reliable grounded responses, with controllable agent policies through
its expressive specification, KITA Worksheet. In contrast to dialog trees, it
is resilient to diverse user queries, helpful with knowledge sources, and
offers ease of programming policies through its declarative paradigm. Through a
real-user study involving 62 participants, we show that KITA beats the GPT-4
with function calling baseline by 26.1, 22.5, and 52.4 points on execution
accuracy, dialogue act accuracy, and goal completion rate, respectively. We
also release 22 real-user conversations with KITA manually corrected to ensure
accuracy.

KITA 是一种可编程框架，用于创建处理复杂用户交互的面向任务的对话代理，通过其声明式范式可提供可靠的有根据的响应和可控的代理策略。

基于 LLM 的开放领域综合任务和知识助手与可编程策略

LLM-Based Open-Domain Integrated Task and Knowledge Assistants with  Programmable Policies

Answer Set Programming (ASP) is a well-established declarative paradigm. One
of the successes of ASP is the availability of efficient systems.
State-of-the-art systems are based on the ground+solve approach. In some
applications this approach is infeasible because the grounding of one or few
constraints is expensive. In this paper, we systematically compare alternative
strategies to avoid the instantiation of problematic constraints, that are
based on custom extensions of the solver. Results on real and synthetic
benchmarks highlight some strengths and weaknesses of the different strategies.
(Under consideration for acceptance in TPLP, ICLP 2017 Special Issue.)

本研究探讨了基于自定义扩展求解器的几种策略，以避免实例化复杂约束条件，对真实和合成基准测试的结果进行了系统比较。