利用稀疏特徵層級限制進行直接偏好優化
Direct Preference Optimization Using Sparse Feature-Level Constraints
November 12, 2024
作者: Qingyu Yin, Chak Tou Leong, Hongbo Zhang, Minjun Zhu, Hanqi Yan, Qiang Zhang, Yulan He, Wenjie Li, Jun Wang, Yue Zhang, Linyi Yang
cs.AI
摘要
大型語言模型(LLMs)與人類偏好的對齊仍然是一個關鍵挑戰。雖然像是從人類反饋中進行強化學習(RLHF)和直接偏好優化(DPO)等訓練後技術已經取得顯著成功,但它們通常會引入計算效率低和訓練不穩定的問題。在本文中,我們提出了基於特徵約束的偏好優化(FPO),這是一種旨在簡化對齊過程並確保穩定性的新方法。FPO利用預先訓練的稀疏自編碼器(SAEs)並引入特徵級約束,從而實現高效、稀疏強制對齊。我們的方法通過使用在訓練良好的稀疏自編碼器中激活的稀疏特徵以及使用特徵級離線參考的順序KL散度,實現了效率。在基準數據集上的實驗結果表明,與最先進的基準相比,FPO在勝率上實現了5.08%的絕對改善,並且計算成本更低,這使其成為一種有效且可控的LLM對齊的有前途的解決方案。
English
The alignment of large language models (LLMs) with human preferences remains
a key challenge. While post-training techniques like Reinforcement Learning
from Human Feedback (RLHF) and Direct Preference Optimization (DPO) have
achieved notable success, they often introduce computational inefficiencies and
training instability. In this paper, we propose Feature-level constrained
Preference Optimization (FPO), a novel method designed to simplify the
alignment process while ensuring stability. FPO leverages pre-trained Sparse
Autoencoders (SAEs) and introduces feature-level constraints, allowing for
efficient, sparsity-enforced alignment. Our approach enjoys efficiency by using
sparse features activated in a well-trained sparse autoencoder and the quality
of sequential KL divergence by using the feature-level offline reference.
Experimental results on benchmark datasets demonstrate that FPO achieves a
5.08% absolute improvement in win rate with much lower computational cost
compared to state-of-the-art baselines, making it a promising solution for
efficient and controllable LLM alignments.Summary
AI-Generated Summary
1比特LLM時代:所有大型語言模型都在1.58比特。The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
1比特LLM時代:所有大型語言模型都在1.58比特。
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