Alignment For Performance Improvement in Conversation Bots
<|reference_start|>Alignment For Performance Improvement in Conversation Bots: 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.<|reference_end|>
@article{garg2024alignment,
    title={Alignment For Performance Improvement in Conversation Bots},
    author={Raghav Garg, Kapil Sharma, Shrey Singla},
    journal={arXiv preprint arXiv:2406.18954},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18954},
    primaryClass={cs.LG cs.AI}
}