The Distracting Effect: Understanding Irrelevant Passages in RAG
Authors: name: Chen Amiraz, name: Florin Cuconasu, name: Simone Filice, name: Zohar Karnin

A well-known issue with Retrieval Augmented Generation (RAG) is that
retrieved passages that are irrelevant to the query sometimes distract the
answer-generating LLM, causing it to provide an incorrect response. In this
paper, we shed light on this core issue and formulate the distracting effect of
a passage w.r.t. a query (and an LLM). We provide a quantifiable measure of the
distracting effect of a passage and demonstrate its robustness across LLMs.
  Our research introduces novel methods for identifying and using hard
distracting passages to improve RAG systems. By fine-tuning LLMs with these
carefully selected distracting passages, we achieve up to a 7.5% increase in
answering accuracy compared to counterparts fine-tuned on conventional RAG
datasets. Our contribution is two-fold: first, we move beyond the simple binary
classification of irrelevant passages as either completely unrelated vs.
distracting, and second, we develop and analyze multiple methods for finding
hard distracting passages. To our knowledge, no other research has provided
such a comprehensive framework for identifying and utilizing hard distracting
passages.