Retrieval Augmented Generation

IEEE Computer Society: Retrieval Quality, Not LLM Size, Now Drives AI Product Performance

IEEE Computer Society highlights that most AI products rely on large language models for reasoning and generation, but these models depend heavily on retrieving relevant background information before producing answers. When that retrieved data is outdated, weak, or irrelevant, the resulting output suffers regardless of the model's sophistication.

wpcms.computer.org · 2026-09-17

Amazon researchers unveil Ising-model method to fix correlated LLM judge panels

Amazon Science researchers, together with Shiva Kasiviswanathan, presented a new paper at ICML that tackles a flaw in using multiple LLM judges to evaluate outputs like retrieval passages: when judges share prompts, training data, or model lineage, their agreement can look stronger than it actually is. Their method models correlations between judges using Ising models and reweights votes to account for shared blind spots rather than treating every vote as independent evidence.

amazon.science · 2026-09-14