Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912068606623744 |
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| author | Guo, Yiju Cui, Ganqu Yuan, Lifan Ding, Ning Sun, Zexu Sun, Bowen Chen, Huimin Xie, Ruobing Zhou, Jie Lin, Yankai Liu, Zhiyuan Sun, Maosong |
| author_facet | Guo, Yiju Cui, Ganqu Yuan, Lifan Ding, Ning Sun, Zexu Sun, Bowen Chen, Huimin Xie, Ruobing Zhou, Jie Lin, Yankai Liu, Zhiyuan Sun, Maosong |
| contents | Alignment in artificial intelligence pursues the consistency between model responses and human preferences as well as values. In practice, the multifaceted nature of human preferences inadvertently introduces what is known as the "alignment tax" -a compromise where enhancements in alignment within one objective (e.g.,harmlessness) can diminish performance in others (e.g.,helpfulness). However, existing alignment techniques are mostly unidirectional, leading to suboptimal trade-offs and poor flexibility over various objectives. To navigate this challenge, we argue the prominence of grounding LLMs with evident preferences. We introduce controllable preference optimization (CPO), which explicitly specifies preference scores for different objectives, thereby guiding the model to generate responses that meet the requirements. Our experimental analysis reveals that the aligned models can provide responses that match various preferences among the "3H" (helpfulness, honesty, harmlessness) desiderata. Furthermore, by introducing diverse data and alignment goals, we surpass baseline methods in aligning with single objectives, hence mitigating the impact of the alignment tax and achieving improvements in multi-objective alignment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_19085 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment Guo, Yiju Cui, Ganqu Yuan, Lifan Ding, Ning Sun, Zexu Sun, Bowen Chen, Huimin Xie, Ruobing Zhou, Jie Lin, Yankai Liu, Zhiyuan Sun, Maosong Computation and Language Artificial Intelligence Systems and Control Alignment in artificial intelligence pursues the consistency between model responses and human preferences as well as values. In practice, the multifaceted nature of human preferences inadvertently introduces what is known as the "alignment tax" -a compromise where enhancements in alignment within one objective (e.g.,harmlessness) can diminish performance in others (e.g.,helpfulness). However, existing alignment techniques are mostly unidirectional, leading to suboptimal trade-offs and poor flexibility over various objectives. To navigate this challenge, we argue the prominence of grounding LLMs with evident preferences. We introduce controllable preference optimization (CPO), which explicitly specifies preference scores for different objectives, thereby guiding the model to generate responses that meet the requirements. Our experimental analysis reveals that the aligned models can provide responses that match various preferences among the "3H" (helpfulness, honesty, harmlessness) desiderata. Furthermore, by introducing diverse data and alignment goals, we surpass baseline methods in aligning with single objectives, hence mitigating the impact of the alignment tax and achieving improvements in multi-objective alignment. |
| title | Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment |
| topic | Computation and Language Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2402.19085 |