Aligning Large Language Models with Representation Editing: A Control Perspective

RE-CONTROL introduces a method for aligning large language models with human objectives by dynamically editing their representations, treating LLMs as stochastic dynamical systems and optimizing control signals through a value function, offering an efficient alternative to fine-tuning that requires fewer computational resources.

Abstract

Aligning large language models (LLMs) with human objectives is crucial for real-world applications. However, fine-tuning LLMs for alignment often suffers from unstable training and requires substantial computing resources. Test-time alignment techniques, such as prompting and guided decoding, do not modify the underlying model, and their performance remains dependent on the original model's capabilities. To address these challenges, we propose aligning LLMs through representation editing. The core of our method is to view a pre-trained autoregressive LLM as a discrete-time stochastic dynamical system. To achieve alignment for specific objectives, we introduce external control signals into the state space of this language dynamical system. We train a value function directly on the hidden states according to the Bellman equation, enabling gradient-based optimization to obtain the optimal control signals at test time. Our experiments demonstrate that our method outperforms existing test-time alignment techniques while requiring significantly fewer resources compared to fine-tuning methods.

Method

The authors used a methodology that involves viewing pre-trained autoregressive large language models (LLMs) as discrete-time stochastic dynamical systems. They introduced control signals into the state space of these language dynamical systems to achieve specific alignment objectives. A value function was trained directly on the hidden states of the LLMs according to the Bellman equation, enabling gradient-based optimization to obtain the optimal control signals at test time. This approach allows for dynamic representation editing and offers a flexible method for aligning LLMs without the need for extensive fine-tuning, which typically requires substantial computational resources.

Main Finding

The authors discovered that their proposed method, RE-CONTROL, which aligns large language models (LLMs) through representation editing, outperforms existing test-time alignment techniques and requires significantly fewer resources compared to fine-tuning methods. Their experiments demonstrated that RE-CONTROL can effectively steer LLMs towards specific objectives by dynamically perturbing the representation space during the autoregressive generation process. This method also exhibits strong generalization capabilities, maintaining performance even when tested on out-of-distribution data.

Conclusion

The conclusion of the paper is that the proposed RE-CONTROL method provides an efficient and effective way to align large language models with human objectives by dynamically editing their representations at test time. This approach offers a flexible alternative to traditional fine-tuning methods, which are resource-intensive and less adaptable to evolving datasets and emerging needs. The authors empirically showed that RE-CONTROL outperforms various existing test-time alignment methods and exhibits strong generalization ability, making it a promising solution for deploying LLMs in real-world applications where rapid adaptability and alignment with human values are crucial.

Keywords

Large Language Models (LLMs), Alignment, Representation Editing, Control Perspective, Bellman Equation, Value Function, Optimal Control, Test-time Alignment, Fine-tuning, Discrete-time Stochastic Dynamical System, Autoregressive Generation, Hidden States, Perturbation, Regularization, Generalization Ability, RE-CONTROL, Value Model, Language Dynamical System, Control Signals, Policy Iteration, Reward Function, Diversity, Coherence, Average Reward, Win Rate, GPT-4 Evaluation, Out-of-distribution Data, Hyperparameter Study, Limitations, Future Work, Broader Impacts, Computing Infrastructure, HH-RLHF, HarmfulQA, Prompt Engineering, Guided Decoding, Static Representation Editing, Control Theory, Reinforcement Learning from Human Feedback (RLHF), Proximal Policy Optimization (PPO), Direct Preference Optimization (DPO), Contrastive Preference Optimization (CPO), Rejection Sampling, Supervised Fine-tuning (SFT), Activation Perturbation, Steering Vectors.