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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2406.18954 |
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| _version_ | 1866910504037908480 |
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| author | Garg, Raghav Sharma, Kapil Singla, Shrey |
| author_facet | Garg, Raghav Sharma, Kapil Singla, Shrey |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18954 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Alignment For Performance Improvement in Conversation Bots Garg, Raghav Sharma, Kapil Singla, Shrey Machine Learning Artificial Intelligence 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. |
| title | Alignment For Performance Improvement in Conversation Bots |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.18954 |