Injecting Salesperson’s Dialogue Strategies in Large Language Models

The article focusses on [https://arxiv.org/pdf/2404.18564], published on 29th April 2024.

Recent studies highlight the persuasive abilities of LLMs. They can smoothly switch from casual conversations to sales pitches, picking up on customer interests subtly. By understanding the listener’s perspective, they can anticipate objections and counter with strong arguments.

The potential is vast. Picture an AI sales assistant deeply knowledgeable about your product, engaging each customer uniquely, and addressing objections until they can’t resist buying. And that is the goal. Create a conversational agent that can steer the discussion towards determining whether the user is interested in receiving recommendations.

Recent research focused on two types of conversations: task-oriented (TOD) and open-domain (chit-chat). TOD helps with specific tasks, while chit-chat aims for fun talks. But in real life, people’s intentions are often GRADUALLY revealed during talks. The term ‘gradually’ is important. And understanding user’s intent is important. It can help us force persuasion. Give them an offer they can’t refuse.

So the work I am discussing in this article focuses on identifying potential intents, which…