PMoL: Parameter Efficient MoE for Preference Mixing of LLM Alignment
This addresses the challenge of handling multiple preferences in LLM alignment, which is incremental as it adapts existing techniques to a specific bottleneck.
The paper tackles the problem of aligning large language models with multiple competing human preferences by proposing PMoL, a parameter-efficient architecture combining Mixture of Experts and Low Rank Adaptors, which achieves superior preference mixing and better alignment with lower training costs compared to baselines.
Reinforcement Learning from Human Feedback (RLHF) has been proven to be an effective method for preference alignment of large language models (LLMs) and is widely used in the post-training process of LLMs. However, RLHF struggles with handling multiple competing preferences. This leads to a decrease in the alignment of LLMs with human preferences. To address this issue, we propose Preference Mixture of LoRAs (PMoL) from the perspective of model architecture, which can adapt to any number of preferences to mix. PMoL combines Mixture of Experts (MoE) and Low Rank Adaptor (LoRA). This architecture is innovatively applied to the research of preference alignment and has achieved significant performance improvement. The expert group soft loss is used to enable MoE with the ability to mix preferences. Through comprehensive evaluation by the reward model and GPT-4o, the experiment results show that PMoL has superior preference mixing capabilities compared to baseline methods. PMoL achieves better preference alignment with lower training costs.