Alignment For Performance Improvement in Conversation Bots

TL;DR

This paper demonstrates that alignment techniques significantly enhance conversational bots' adherence to predefined guardrails, outperforming instruction fine-tuning alone, especially in domains demanding strict rule compliance.

Contribution

It compares traditional instruction fine-tuning with recent alignment methods like IPO and KTO, highlighting their effectiveness in improving guardrail adherence in chatbots.

Findings

Alignment methods outperform fine-tuning in adherence to guardrails.

Alignment techniques are effective both before and after instruction tuning.

Enhanced compliance in customer care domains.

Abstract

This paper shows that alignment methods can achieve superior adherence to guardrails compared to instruction fine-tuning alone in conversational agents, also known as bots, within predefined guidelines or 'guardrails'. It examines traditional training approaches such as instruction fine-tuning and the recent advancements in direct alignment methods like Identity Preference Optimization (IPO), and Kahneman-Tversky Optimization (KTO). The effectiveness of alignment techniques both pre and post-instruction tuning is highlighted, illustrating their potential to optimize conversational bots in domains that require strict adherence to specified rules, such as customer care.