Collapse of Dense Retrievers: Short, Early, and Literal Biases
  Outranking Factual Evidence
Authors: name: Mohsen Fayyaz, name: Ali Modarressi, name: Hinrich Schuetze, name: Nanyun Peng

Dense retrieval models are commonly used in Information Retrieval (IR)
applications, such as Retrieval-Augmented Generation (RAG). Since they often
serve as the first step in these systems, their robustness is critical to avoid
failures. In this work, by repurposing a relation extraction dataset (e.g.
Re-DocRED), we design controlled experiments to quantify the impact of
heuristic biases, such as favoring shorter documents, in retrievers like
Dragon+ and Contriever. Our findings reveal significant vulnerabilities:
retrievers often rely on superficial patterns like over-prioritizing document
beginnings, shorter documents, repeated entities, and literal matches.
Additionally, they tend to overlook whether the document contains the query's
answer, lacking deep semantic understanding. Notably, when multiple biases
combine, models exhibit catastrophic performance degradation, selecting the
answer-containing document in less than 3% of cases over a biased document
without the answer. Furthermore, we show that these biases have direct
consequences for downstream applications like RAG, where retrieval-preferred
documents can mislead LLMs, resulting in a 34% performance drop than not
providing any documents at all.