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arXiv · 2406.18954

Alignment For Performance Improvement in Conversation Bots

Raghav Garg·Kapil Sharma·Shrey Singla
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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.

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Raghav Garg, Kapil Sharma, Shrey Singla. 2024-06-27. Alignment For Performance Improvement in Conversation Bots. https://arxiv.org/abs/2406.18954

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