Enhancing Human-LLM Alignment Through Contrastive Learning

The CLHA framework addresses a critical aspect of AI development — ensuring that Large Language Models align with human preferences. This work presents a direct way to promote this alignment, leveraging adaptive fine-tuning and contrastive loss strategies.

• CLHA uses a rescoring strategy to assess and mitigate noise in the data.
• It adapts the likelihood of LLMs generating responses that match human expectations.
• The framework was tested on the ‘Helpful and Harmless’ dataset and displayed superior alignment results.
• CLHA proposes an improved approach for making AI systems more beneficial and intelligible to users.

The significance of this paper lies in its straightforward yet effective solution to a longstanding AI challenge. By focusing on human-aligned LLM output, CLHA has the potential to facilitate more responsible AI usage and pave the way for advancements in user-oriented AI applications.