Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID
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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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- 2024EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation17 Dec 2024
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12 most cited of 16 All 16 (JSON)
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- 2026Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMs12 May 2026
- 2026Hypencoder Revisited: Reproducibility and Analysis of Non-Linear Scoring for First-Stage Retrieval29 Apr 2026
- 2026100x Cost & Latency Reduction: Performance Analysis of AI Query Approximation using Lightweight Proxy Models16 Mar 2026
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- 2026Modular Expert Merging for Biomedical Retrieval4 Feb 2026
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- 2025When Embedding Models Meet: Procrustes Bounds and Applications15 Oct 2025
- 2025Benchmarking Information Retrieval Models on Complex Retrieval Tasks8 Sep 2025
- 2025Hypencoder: Hypernetworks for Information Retrieval7 Feb 2025
12 newest of 16 All 16 (JSON)