2 papers
cs.IR2026
Less LLM, More Documents: Searching for Improved RAG
Jingjie Ning, Yibo Kong, Yunfan Long +1
Retrieval-Augmented Generation (RAG) couples document retrieval with large language models (LLMs). While scaling generators often improves accuracy, it also increases inference and…
cs.IR2024
Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval
João Coelho, Bruno Martins, João Magalhães +2
This study investigates the existence of positional biases in Transformer-based models for text representation learning, particularly in the context of web document retrieval. We b…