Is Free Self-Alignment Possible?
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910838477029376 |
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| author | Adila, Dyah Shin, Changho Zhang, Yijing Sala, Frederic |
| author_facet | Adila, Dyah Shin, Changho Zhang, Yijing Sala, Frederic |
| contents | Aligning pretrained language models (LMs) often requires large-scale preference data and substantial computational resources. These costs become even more prohibitive for multi-objective or pluralistic alignment. Is this truly necessary? Can we perform efficient alignment using only internal model capabilities, and without additional training? To answer this question, we propose AlignEZ, a novel approach that leverages (1) self-generated preference data and (2) representation editing to achieve cost-effective, efficient alignment. By operating directly on learned representations, AlignEZ independently targets different behavioral aspects without the overhead of traditional alignment methods. Our experiments reveal that this cost-efficient procedure improves performance across diverse tasks: up to 19.9% on general alignment and 1.9% on challenging mathematical reasoning tasks, even when starting from a strong base model. AlignEZ can also align models to multiple objectives simultaneously, granting fine-grained control over multiple preference axes. Finally, we show that AlignEZ can accelerate more expensive alignment procedures--such as DPO--even under limited availability of ground-truth preference data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03642 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Is Free Self-Alignment Possible? Adila, Dyah Shin, Changho Zhang, Yijing Sala, Frederic Computation and Language Machine Learning Aligning pretrained language models (LMs) often requires large-scale preference data and substantial computational resources. These costs become even more prohibitive for multi-objective or pluralistic alignment. Is this truly necessary? Can we perform efficient alignment using only internal model capabilities, and without additional training? To answer this question, we propose AlignEZ, a novel approach that leverages (1) self-generated preference data and (2) representation editing to achieve cost-effective, efficient alignment. By operating directly on learned representations, AlignEZ independently targets different behavioral aspects without the overhead of traditional alignment methods. Our experiments reveal that this cost-efficient procedure improves performance across diverse tasks: up to 19.9% on general alignment and 1.9% on challenging mathematical reasoning tasks, even when starting from a strong base model. AlignEZ can also align models to multiple objectives simultaneously, granting fine-grained control over multiple preference axes. Finally, we show that AlignEZ can accelerate more expensive alignment procedures--such as DPO--even under limited availability of ground-truth preference data. |
| title | Is Free Self-Alignment Possible? |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2406.03642 |