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Promptagator: Few-shot Dense Retrieval From 8 Examples

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2209.11755

Summary

Dense retrievers are usually trained on large QA collections like MS MARCO or Natural Questions and then applied to other tasks. This assumes that one notion of relevance fits every task. The authors argue that this is false: retrieval tasks differ in search intent and query style, even when they share a corpus. They define a Few-shot BEIR setting in which each task provides only a brief description and 2–8 labeled query-document pairs.

Their method, Promptagator, does not train on these examples directly. Instead, it puts them into task-specific prompts for FLAN, which writes synthetic queries for documents in the target corpus. The noisy pairs are then cleaned with a round-trip consistency filter, using a retriever trained only on the generated data. The filtered data trains compact dual encoders and, optionally, a cross-attention reranker.

Without any MS MARCO or NQ supervision, these retrievers beat heavily engineered models such as ColBERT v2 and SPLADE v2 on 11 BEIR datasets. This suggests that a handful of in-task examples, amplified by an LLM, can replace large annotated corpora while keeping serving cheap.

Key points

  • Few-shot BEIR protocol: 2–8 relevant pairs are sampled per dataset, from the dev split where one exists. When examples come from the test set, they are scored as misses during evaluation. The prompts and examples are to be made public.
  • Retriever results: Few-shot Promptagator dual encoders exceed ColBERT v2 by over 1.2 nDCG@10 on average across 11 BEIR datasets.
  • Reranker results: A standard-size reranker trained on the same synthetic data adds about 5.0 more nDCG points (the Promptagator++ variant).
  • Task-specific prompts: Each prompt is built from document and query descriptions. For example, ArguAna pairs "Argument" with "Counter Argument," and outputs that break this format are discarded.
  • Efficiency: The approach avoids the high cost of using an LLM at search time, unlike approaches built on 12k-dimensional GPT-3 embeddings or LLM-based reranking.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

124 papers of our library cite it (of the 140673 whose bibliographies we read); 7 of them have a page here, 117 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2209.11755 · arXiv 2209.11755

@misc{dai2022promptagator,
  title = {{Promptagator: Few-shot Dense Retrieval From 8 Examples}},
  author = {Dai, Zhuyun and Zhao, Vincent Y. and Ma, Ji and Luan, Yi and Ni, Jianmo and Lu, Jing and Bakalov, Anton and Guu, Kelvin and Hall, Keith B. and Chang, Ming-Wei},
  year = {2022},
  doi = {10.48550/arxiv.2209.11755},
  eprint = {2209.11755},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2209.11755}
}

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