@inproceedings{guo-etal-2024-controllable,
title = "Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment",
author = "Guo, Yiju and
Cui, Ganqu and
Yuan, Lifan and
Ding, Ning and
Sun, Zexu and
Sun, Bowen and
Chen, Huimin and
Xie, Ruobing and
Zhou, Jie and
Lin, Yankai and
Liu, Zhiyuan and
Sun, Maosong",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.85",
doi = "10.18653/v1/2024.emnlp-main.85",
pages = "1437--1454",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment
%A Guo, Yiju
%A Cui, Ganqu
%A Yuan, Lifan
%A Ding, Ning
%A Sun, Zexu
%A Sun, Bowen
%A Chen, Huimin
%A Xie, Ruobing
%A Zhou, Jie
%A Lin, Yankai
%A Liu, Zhiyuan
%A Sun, Maosong
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F guo-etal-2024-controllable
%X 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.
%R 10.18653/v1/2024.emnlp-main.85
%U https://aclanthology.org/2024.emnlp-main.85
%U https://doi.org/10.18653/v1/2024.emnlp-main.85
%P 1437-1454
Markdown (Informal)
[Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment](https://aclanthology.org/2024.emnlp-main.85) (Guo et al., EMNLP 2024)
ACL
- Yiju Guo, Ganqu Cui, Lifan Yuan, Ning Ding, Zexu Sun, Bowen Sun, Huimin Chen, Ruobing Xie, Jie Zhou, Yankai Lin, Zhiyuan Liu, and Maosong Sun. 2024. Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1437–1454, Miami, Florida, USA. Association for Computational Linguistics.