Retcon通过提示词实现对话中每一轮的精准控制。
Retcon -- a Prompt-Based Technique for Precise Control of LLMs in Conversations
- 用少量示例提示实现每轮对话的动态行为控制
- 相比零样本和传统少样本提示，效果显著提升
- 适合需要灵活调整角色或策略的交互式应用
大型语言模型（LLMs）已能执行复杂的自然语言任务。许多应用场景如客服助手、教学助理和互动机器人涉及多轮对话。然而，在此类交互中精确控制LLM行为仍具挑战性，尤其当模型行为需随对话进程动态调整时。本文提出Retcon——一种基于提示的少样本技术，可在对话中实现逐轮级别的控制。实验表明，Retcon在性能上显著优于零样本及传统少样本提示方法。
原文摘要 · Abstract (English)
Recent advances in Large Language Models (LLMs) allow agents to execute complex natural language tasks. Many LLM applications, such as support agents, teaching assistants, and interactive bots, involve multi-turn conversations. However, it remains challenging to control LLMs in the context of such interactions, particularly when the LLM behavior needs to be adjustable over the course of the conversation. In this paper, we present Retcon, a few-shot prompting technique designed to provide turn-level control over LLMs in conversations. We then demonstrate that it performs significantly better than zero-shot and traditional few-shot prompting.
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