Alignment For Performance Improvement in Conversation Bots
This addresses the need for strict rule-following in domains like customer care, though it appears incremental as it builds on existing alignment techniques.
The paper tackles the problem of improving conversational agents' adherence to predefined guardrails, showing that alignment methods achieve superior performance compared to instruction fine-tuning alone.
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.