Collapse of Dense Retrievers: Short, Early, and Literal Biases
  Outranking Factual Evidence
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.
67d0c4be13d767fbbfaffe94
Mohsen Fayyaz
admin_assigned
2025-03-12T22:03:29.044Z
60465ecd4f0c980fda768da7
https://cdn-avatars.huggingface.co/v1/production/uploads/1648301912044-60465ecd4f0c980fda768da7.jpeg
Mohsen Fayyaz
user
mohsenfayyaz
67d0c4be13d767fbbfaffe95
Ali Modarressi
claimed_verified
2025-03-12T20:46:08.879Z
60e4738a8c0ddd18fc27ff88
https://cdn-avatars.huggingface.co/v1/production/uploads/60e4738a8c0ddd18fc27ff88/lpLeeIW8r85RTY4fGZTva.jpeg
Ali Modarressi
user
amodaresi
67d0c4be13d767fbbfaffe96
Hinrich Schuetze
67d0c4be13d767fbbfaffe97
Nanyun Peng
Collapse of Dense Retrievers: Short, Early, and Literal Biases
  Outranking Factual Evidence
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.
Studies on dense retrieval models reveal significant heuristic biases affecting performance and semantic understanding, leading to severe degradation in tasks like Retrieval-Augmented Generation.
dense retrieval models Retrieval-Augmented Generation RAG relation extraction Dragon+ Contriever heuristic biases document beginnings shorter documents repeated entities literal matches deep semantic understanding performance degradation LLMs