Towards Zero-Shot, Controllable Dialog Planning with LLMs

TL;DR

This paper presents a zero-shot, controllable dialog planning method using LLMs and dialog graphs, outperforming RL-based agents in simulation and real user tasks, especially in sensitive domains.

Contribution

Introduces a novel zero-shot approach for controllable Conversational Tree Search using LLM-guided graph search, eliminating the need for extensive training.

Findings

Significantly outperforms state-of-the-art CTS agents in simulation ($p<0.0001$).

Improves task success in real user evaluation ($p<0.05$).

Generalizes across all CTS domains.

Abstract

Recently, Large Language Models (LLMs) have emerged as an alternative to training task-specific dialog agents, due to their broad reasoning capabilities and performance in zero-shot learning scenarios. However, many LLM-based dialog systems fall short in planning towards an overarching dialog goal and therefore cannot steer the conversation appropriately. Furthermore, these models struggle with hallucination, making them unsuitable for information access in sensitive domains, such as legal or medical domains, where correctness of information given to users is critical. The recently introduced task Conversational Tree Search (CTS) proposes the use of dialog graphs to avoid hallucination in sensitive domains, however, state-of-the-art agents are Reinforcement Learning (RL) based and require long training times, despite excelling at dialog strategy. This paper introduces a novel zero-shot method for controllable CTS agents, where LLMs guide the dialog planning through domain graphs by searching and pruning relevant graph nodes based on user interaction preferences. We show that these agents significantly outperform state-of-the-art CTS agents ($p<0.0001$; Barnard Exact test) in simulation. This generalizes to all available CTS domains. Finally, we perform user evaluation to test the agent's performance in the wild, showing that our policy significantly ($p<0.05$; Barnard Exact) improves task-success compared to the state-of-the-art RL-based CTS agent.