Researchers from AWS AI Labs and USC Propose DeAL: A Machine Learning Framework that Allows the User to Customize Reward Functions and Enables Decoding-Time Alignment of LLMs

A crucial challenge at the core of the advancements in large language models (LLMs) is ensuring that their outputs align with human ethical standards and intentions. Despite their sophistication, these models can generate content that can be technically accurate but may not align with specific user expectations or societal norms. This misalignment highlights the need for effective mechanisms to guide LLM outputs toward desired ethical and practical objectives, posing a significant hurdle in harmonizing machine-generated content with human values and intentions.

Current methods to address this alignment challenge primarily focus on modifying the training process of these models, employing techniques like Reinforcement Learning with Human Feedback (RLHF). However, these approaches are limited by their reliance on static, predefined reward functions and their inability to adapt to nuanced or evolving human preferences.

Researchers have introduced a novel framework, DeAL (Decoding-time Alignment for Large Language Models), that reimagines the approach to model alignment by allowing for the customization of reward functions at the decoding stage rather than during training. This innovation provides a more flexible and dynamic method for aligning model outputs with specific user objectives.

Navigating this search involves utilizing the A* search algorithm powered by an auto-regressive LLM. This system is finely tuned through hyper-parameters and a heuristic function designed to approximate the alignment rewards, optimizing the generation outcomes. As the search unfolds, the agent dynamically adapts the start state, tweaking the input prompt to refine generation results further. An important step in this process is action selection, where a select group of candidate actions is chosen based on their likelihood, as determined by the LLM. This approach is strengthened by alignment metrics serving as heuristics to assess each action’s potential, with lookahead mechanisms offering valuable insights on the most promising paths. The decision for the subsequent action hinges on a scoring function that integrates the action’s probability with the heuristic score, allowing for a choice between deterministic and stochastic methods. This framework’s versatility extends to accommodating programmatically verifiable constraints and parametric estimators as heuristics, addressing the gap left by previous works in considering parametric alignment objectives for LLMs.

The experiments showcase DeAL’s ability to enhance alignment to objectives across varied scenarios without compromising task performance. From keyword-constrained generation tasks demonstrating improved keyword coverage in the CommonGen dataset to length-constrained summarization tasks in the XSUM dataset showing better length satisfaction, DeAL proves superior. It excels in scenarios requiring abstract alignment objectives like harmlessness and helpfulness, offering a flexible and effective solution, particularly in security situations. DeAL’s ability to be calibrated for specific alignment levels further underscores its adaptability and effectiveness compared to traditional methods.

In conclusion, DeAL represents a remarkable advancement in the quest for more aligned and ethically conscious AI models. By integrating with current alignment strategies like system prompts and fine-tuning, DeAL boosts alignment quality. It emerges as a pivotal solution in security contexts, overcoming the limitations of traditional methods that struggle with incorporating multiple custom rewards and the subjective biases of developers. Experimental evidence supports DeAL’s effectiveness in refining alignment, addressing LLMs’ residual gaps, and managing nuanced trade-offs, marking a significant advancement in ethical AI development.

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