Building on part 1’s exploration of naive RAG’s limitations, this post introduces adaptive retrieval frameworks and pre-generation retrieval decision-making methods that determine if retrieval is truly necessary.
Retrieval-Augmented Generation (RAG) isn’t a silver bullet. This post highlights the hidden costs associated with RAG and makes the case for a smarter, adaptive approach.
Learned embeddings often suffer from ’embedding collapse’, where they occupy only a small subspace of the available dimensions. This article explores the causes of embedding collapse, from two-tower models to GNN-based systems, and its impact on model scalability and recommendation quality. We discuss methods to detect collapse and examine recent solutions proposed by research teams at Visa, Facebook AI, and Tencent Ads to address this challenge.
This article provides an introduction to online advertising systems and explores research work that incorporates ads into the LLM responses to user queries of commercial nature.
This article continues the discussion on the evolution of multi-task learning-based large-scale recommender systems. We take a look at strategies from Kuaishou, Tencent, YouTube, Facebook, and Amazon Prime Video to disentangle input space and address systematic biases. The article ends with sharing several tips and learnings for professionals working in this domain.
This article introduces the multi-task learning paradigm adopted by various large-scale video recommender systems. It introduces a general setup for such an MTL-based recommender. It highlights several associated challenges and describes solutions adopted by various state-of-the-art recommenders in the industry.
