Attention Is All You Have

● Background : The rapid proliferation of Artificial Intelligence (AI) models has led to an "AI deluge," characterized by an overwhelming volume of AI-generated information. Users interacting with multiple AI systems for a single query often face conflicting, low-quality, or misleading responses, inducing cognitive overload and hampering their ability to discern optimal and reliable information. This situation underscores the urgent need for effective mechanisms to curate and present AI-generated content. ● Methods : This paper introduces "Attention Is All You Have" (AIAYH), a novel computational method for ranking responses from diverse AI providers. AIAYH adapts the core principles of the Transformer's attention mechanism, as described by Vaswani et al. (2017), to this new domain. Specifically, it redefines the Query, Key, and Value (QKV) components to assess the relevance and quality of multiple, complete AI responses relative to a user's specific query. A Multi-Head Response Attention module is employed to concurrently evaluate various quality dimensions, such as semantic relevance, factual consistency, and coherence. ● Results : The efficacy of the AIAYH method was evaluated through simulated testing on a representative dataset. Results indicate that AIAYH significantly outperforms baseline methods—including random selection, lexical overlap, and a simplified LLM-as-a-judge approach—in ranking AI responses according to predefined metrics such as Normalized Discounted Cumulative Gain (NDCG), Mean Reciprocal Rank (MRR), and Top-1 accuracy. Ablation studies further suggest the positive contribution of the multi-head architecture to nuanced response evaluation. ● Conclusions : The AIAYH method demonstrates considerable potential as a robust solution for mitigating information overload and enhancing user interaction with multi-source AI systems. By providing a principled way to identify the most pertinent AI response, AIAYH can improve the reliability and utility of AI-generated information, thereby empowering users in an increasingly AI-driven landscape.