mlsanityData to 7 Oct 2026

Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID

arXiv 2104.09632 ↗First posted 19 Apr 2021cs.IRcited by 19

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Abstract (arXiv)

We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19. The goals of TREC-COVID include the construction of a pandemic search test collection and the evaluation of IR methods for COVID-19. The challenge was conducted over five rounds from April to July, 2020, with participation from 92 unique teams and 556 individual submissions. A total of 50 topics (sets of related queries) were used in the evaluation, starting at 30 topics for Round 1 and adding 5 new topics per round to target emerging topics at that state of the still-emerging pandemic. This paper provides a comprehensive overview of the structure and results of TREC-COVID. Specifically, the paper provides details on the background, task structure, topic structure, corpus, participation, pooling, assessment, judgments, results, top-performing systems, lessons learned, and benchmark datasets.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Apr 2021.

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Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 6; 2026: 8.

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  1. 2024Generative Large Language Models in Automated Fact-Checking: A Surveycited by 19
  2. 2024EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generationcited by 17
  3. 2023Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrievalcited by 15
  4. 2022How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generationcited by 7
  5. 2025Hypencoder: Hypernetworks for Information Retrievalcited by 7
  6. 2025Benchmarking Information Retrieval Models on Complex Retrieval Taskscited by 6
  7. 2026100x Cost & Latency Reduction: Performance Analysis of AI Query Approximation using Lightweight Proxy Modelscited by 3
  8. 2025When Embedding Models Meet: Procrustes Bounds and Applicationscited by 1
  9. 2025DIRC-RAG: Accelerating Edge RAG with Robust High-Density and High-Loading-Bandwidth Digital In-ReRAM Computationcited by 1
  10. 2025A Representation Sharpening Framework for Zero Shot Dense Retrievalcited by 1
  11. 2026Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMscited by 1
  12. 2026Your UnEmbedding Matrix is Secretly a Feature Lens for Text Embeddingscited by 1
7 more
  1. 2024Advancing Chinese biomedical text mining with community challengescited by 0
  2. 2025Evaluating Medical LLMs by Levels of Autonomy: A Survey Moving from Benchmarks to Applicationscited by 0
  3. 2026Modular Expert Merging for Biomedical Retrievalcited by 0
  4. 2026SoftMatcha 2: A Fast and Soft Pattern Matcher for Trillion-Scale Corporacited by 0
  5. 2026Hypencoder Revisited: Reproducibility and Analysis of Non-Linear Scoring for First-Stage Retrievalcited by 0
  6. 2026HAKARI-Bench: A Lightweight Benchmark for Comparing Retrieval Architectures and Efficiency Settings under Unified Conditionscited by 0
  7. 2026Retrieval, not hallucinations, will be the limiting factor for LLM-based clinical AI toolscited by 0