FLAP: Flow Adhering Planning with Constrained Decoding in LLMs

Abstract: 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).