# Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision

arXiv: https://arxiv.org/abs/2012.14862 · HTML: https://mlsanity.com/paper/2012.14862/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval](https://mlsanity.com/paper/2311.05800/) (2023)
- [Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey](https://mlsanity.com/paper/2208.03197/) (2022)
- [P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning](https://mlsanity.com/paper/2205.01886/) (2022)
- [CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion](https://mlsanity.com/paper/2212.09114/) (2022)
- [Learning List-Level Domain-Invariant Representations for Ranking](https://mlsanity.com/paper/2212.10764/) (2022)
- [Rethinking Dense Retrieval's Few-Shot Ability](https://mlsanity.com/paper/2304.05845/) (2023)
- [Hybrid and Collaborative Passage Reranking](https://mlsanity.com/paper/2305.09313/) (2023)
- [Domain Adaptation of Multilingual Semantic Search -- Literature Review](https://mlsanity.com/paper/2402.02932/) (2024)

## Abstract (arXiv)

The effectiveness of Neural Information Retrieval (Neu-IR) often depends on a large scale of in-domain relevance training signals, which are not always available in real-world ranking scenarios. To democratize the benefits of Neu-IR, this paper presents MetaAdaptRank, a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains. Drawing on source-domain massive relevance supervision, MetaAdaptRank contrastively synthesizes a large number of weak supervision signals for target domains and meta-learns to reweight these synthetic "weak" data based on their benefits to the target-domain ranking accuracy of Neu-IR models. Experiments on three TREC benchmarks in the web, news, and biomedical domains show that MetaAdaptRank significantly improves the few-shot ranking accuracy of Neu-IR models. Further analyses indicate that MetaAdaptRank thrives from both its contrastive weak data synthesis and meta-reweighted data selection. The code and data of this paper can be obtained from https://github.com/thunlp/MetaAdaptRank.