Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations

Understand

Recent progress on large language models (LLMs) has enabled dialogue agents to generate highly naturalistic and plausible text.

• However, current LLM language generation focuses on responding accurately to questions and requests with a single effective response.
• In reality, many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion.
• Accounting for how an agent can effectively steer a conversation is a crucial ability in many dialogue tasks, from healthcare to preference elicitation.