Planning is a crucial task for agents in task oriented dialogs (TODs). Human
agents typically resolve user issues by following predefined workflows,
decomposing workflow steps into actionable items, and performing actions by
executing APIs in order; all of which require reasoning and planning. With the
recent advances in LLMs, there have been increasing attempts to use LLMs for
task planning and API usage. However, the faithfulness of the plans to
predefined workflows and API dependencies, is not guaranteed with LLMs because
of their bias towards pretraining data. Moreover, in real life, workflows are
custom-defined and prone to change, hence, quickly adapting agents to the
changes is desirable. In this paper, we study faithful planning in TODs to
resolve user intents by following predefined flows and preserving API
dependencies. We propose a constrained decoding algorithm based on lookahead
heuristic for faithful planning. Our algorithm alleviates the need for
finetuning LLMs using domain specific data, outperforms other decoding and
prompting-based baselines, and applying our algorithm on smaller LLMs (7B) we
achieve comparable performance to larger LLMs (30B-40B).

通过预定义的工作流程和 API 依赖关系，基于先知启发式的受限解码算法提供了对面向任务对话中的忠诚规划的研究，优于其他解码和提示为基础的基准，并且在较小的 LLMs（7B）上应用我们的算法，实现了与更大的 LLMs（30B-40B）相当的性能。