The realm of artificial intelligence has witnessed a significant breakthrough with the introduction of the SELF-DISCOVER framework, a novel approach that empowers Large Language Models (LLMs) to autonomously uncover and employ intrinsic reasoning structures. This advancement is poised to redefine how AI systems tackle complex reasoning challenges, offering a more efficient and interpretable method compared to traditional prompting techniques. …
In the ever-evolving landscape of artificial intelligence, a groundbreaking development emerges with "Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution." This paper introduces an innovative approach that pushes the boundaries of how Large Language Models (LLMs) can be enhanced, not through manual tweaks but via an evolutionary mechanism that refines the art of prompting itself. …
The paper presents Hiformer, an innovative Transformer-based model tailored for recommender systems, emphasizing efficient heterogeneous feature interaction learning. Traditional Transformer architectures face significant hurdles in recommender systems, notably in capturing the complex interplay of diverse features and achieving acceptable serving latency for web-scale applications. …
Language models (LMs) have been making remarkable strides in understanding and generating human language. Yet, their true potential in problem-solving tasks has been somewhat limited by the reliance on human-generated data. The groundbreaking paper, "Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models", introduces a novel method named Reinforced Self-Training (ReST) that promises to change this landscape. …