# Investigating the Successes and Failures of BERT for Passage Re-Ranking

arXiv: https://arxiv.org/abs/1905.01758 · HTML: https://mlsanity.com/paper/1905.01758/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2019.

## Built on by (most cited first, 2 of 2)

- [Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval](https://mlsanity.com/paper/2203.03367/) (2022)
- [DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification](https://mlsanity.com/paper/2412.00600/) (2024)

## Abstract (arXiv)

The bidirectional encoder representations from transformers (BERT) model has recently advanced the state-of-the-art in passage re-ranking. In this paper, we analyze the results produced by a fine-tuned BERT model to better understand the reasons behind such substantial improvements. To this aim, we focus on the MS MARCO passage re-ranking dataset and provide potential reasons for the successes and failures of BERT for retrieval. In more detail, we empirically study a set of hypotheses and provide additional analysis to explain the successful performance of BERT.