# TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection

arXiv: https://arxiv.org/abs/2005.04474 · HTML: https://mlsanity.com/paper/2005.04474/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2020.

## Built on by (most cited first, 23 of 23)

- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://mlsanity.com/paper/2112.01488/) (2021)
- [Practical Poisoning Attacks against Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.03957/) (2025)
- [MS MARCO: Benchmarking Ranking Models in the Large-Data Regime](https://mlsanity.com/paper/2105.04021/) (2021)
- [Simplified Data Wrangling with ir_datasets](https://mlsanity.com/paper/2103.02280/) (2021)
- [Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations](https://mlsanity.com/paper/2110.07581/) (2021)
- [Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction](https://mlsanity.com/paper/2203.13088/) (2022)
- [CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example](https://mlsanity.com/paper/2103.12906/) (2021)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature](https://mlsanity.com/paper/2106.13375/) (2021)
- [Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims](https://mlsanity.com/paper/2205.02596/) (2022)
- [Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents](https://mlsanity.com/paper/2302.06854/) (2023)
- [I3: Intent-Introspective Retrieval Conditioned on Instructions](https://mlsanity.com/paper/2308.10025/) (2023)
- [CoRank: LLM-Based Compact Reranking with Document Features for Scientific Retrieval](https://mlsanity.com/paper/2505.13757/) (2025)
- [From Topology to Retrieval: Decoding Embedding Spaces with Unified Signatures](https://mlsanity.com/paper/2511.22150/) (2025)
- [Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval](https://mlsanity.com/paper/2208.05753/) (2022)
- [PANACEA: An Automated Misinformation Detection System on COVID-19](https://mlsanity.com/paper/2303.01241/) (2023)
- [Multivariate Representation Learning for Information Retrieval](https://mlsanity.com/paper/2304.14522/) (2023)
- [Which Neurons Matter in IR? Applying Integrated Gradients-based Methods to Understand Cross-Encoders](https://mlsanity.com/paper/2406.19309/) (2024)
- [m3BERT: A Modern, Multi-lingual, Matryoshka Bidirectional Encoder](https://mlsanity.com/paper/2605.19568/) (2026)
- [Impact of detecting clinical trial elements in exploration of COVID-19 literature](https://mlsanity.com/paper/2105.12261/) (2021)
- [REANIMATOR: Reanimate Retrieval Test Collections with Extracted and Synthetic Resources](https://mlsanity.com/paper/2504.07584/) (2025)
- [Re:CAP - Auditing Retrieval Coverage in Production RAG Pipelines](https://mlsanity.com/paper/2609.24122/) (2026)

## Abstract (arXiv)

TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic. One of the key characteristics of pandemic search is the accelerated rate of change: the topics of interest evolve as the pandemic progresses and the scientific literature in the area explodes. The COVID-19 pandemic provides an opportunity to capture this progression as it happens. TREC-COVID, in creating a test collection around COVID-19 literature, is building infrastructure to support new research and technologies in pandemic search.