# Silver Retriever: Advancing Neural Passage Retrieval for Polish Question Answering

arXiv: https://arxiv.org/abs/2309.08469 · HTML: https://mlsanity.com/paper/2309.08469/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 17 of 17)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking](https://mlsanity.com/paper/2110.07367/) (2021)
- [mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset](https://mlsanity.com/paper/2108.13897/) (2021)
- [HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish](https://mlsanity.com/paper/2105.01735/) (2021)
- [Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval](https://mlsanity.com/paper/2203.05765/) (2022)
- [BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language](https://mlsanity.com/paper/2305.19840/) (2023)
- [Training Effective Neural Sentence Encoders from Automatically Mined Paraphrases](https://mlsanity.com/paper/2207.12759/) (2022)
- [MAUPQA: Massive Automatically-created Polish Question Answering Dataset](https://mlsanity.com/paper/2305.05486/) (2023)

## Built on by (most cited first, 5 of 5)

- [PL-MTEB: Polish Massive Text Embedding Benchmark](https://mlsanity.com/paper/2405.10138/) (2024)
- [PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods](https://mlsanity.com/paper/2402.13350/) (2024)
- [Assessing generalization capability of text ranking models in Polish](https://mlsanity.com/paper/2402.14318/) (2024)
- [Transferring BERT Capabilities from High-Resource to Low-Resource Languages Using Vocabulary Matching](https://mlsanity.com/paper/2402.14408/) (2024)
- [Annotated Surrogate Retrieval for Polish Statutory Law](https://mlsanity.com/paper/2608.30929/) (2026)

## Abstract (arXiv)

Modern open-domain question answering systems often rely on accurate and efficient retrieval components to find passages containing the facts necessary to answer the question. Recently, neural retrievers have gained popularity over lexical alternatives due to their superior performance. However, most of the work concerns popular languages such as English or Chinese. For others, such as Polish, few models are available. In this work, we present Silver Retriever, a neural retriever for Polish trained on a diverse collection of manually or weakly labeled datasets. Silver Retriever achieves much better results than other Polish models and is competitive with larger multilingual models. Together with the model, we open-source five new passage retrieval datasets.