The Philosophy of Information Retrieval Evaluation
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
BrowseComp-Plus facilitates disentangled evaluations by providing a human-verified corpus, expanding the classic Cranfield paradigm to modern deep search evaluation.
“BrowseComp-Plus facilitates disentangled evaluations by providing a human-verified corpus, expanding the classic Cranfield paradigm (Voorhees, 2002) to modern deep search evaluation.”
From BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents (2026) · cites this paper as Voorhees, 2002
Cites
How reliable are the results of large-scale information retrieval experiments?
Zobel, J. (1998) · Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/290941.291014 · Cited by 579 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Building simulated queries for known-item topics: an analysis using six european languages
Azzopardi, L., de Rijke, M., & Balog, K. (2007) · Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1277741.1277820 · Cited by 135 · see citing works
Engineering Quality and Reliability in Technology-Assisted Review
Cormack, G. V., & Grossman, M. R. (2016) · Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval · doi:10.1145/2911451.2911510 · Cited by 98 · see citing works
Who uses web search for what: and how
Weber, I., & Jaimes, A. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935839 · Cited by 91 · see citing works
BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents
Chen, Z., Ma, X., Zhuang, S., Nie, P., Zou, K., Sharifymoghaddam, S., Liu, A., Green, J., Patel, K., Meng, R., et al. (2026) · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2026.acl-long.1023 · Cited by 2 · see citing works
“BrowseComp-Plus facilitates disentangled evaluations by providing a human-verified corpus, expanding the classic Cranfield paradigm (Voorhees, 2002) to modern deep search evaluation.”
Same Ranking, Different Winner: How Scoring Targets Shape LLM Memory Benchmarks
Panthi, S., & Abdelfattah, R. (2026) · arXiv · doi:10.48550/arxiv.2605.24060
“The closest analogy is classical IR work on evaluation sensitivity (Bailey et al., 2008; Yang et al., 2019; Craswell et al., 2020; Voorhees, 2001; Voorhees and Harman, 2005; Manning et al., 2008; Järvelin and Käkäläinen, 2002).”
- Authors
- Voorhees, E. M.
- Affiliations
- National Institute of Standards and Technology (US)
- Corresponding author
- Voorhees, Ellen M.
- ORCID
- Voorhees, Ellen M.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2002
- Pages
- 355-370
- ISSN
- 0302-9743 (ISSN-L)
- OpenAlex
- W1554394657
- Cited by
- 389
- References
- 1
- Citations by year
- 2026: 9 · 2025: 8 · 2024: 8 · 2023: 19 · 2022: 11 · 2021: 15 · 2020: 15 · 2019: 11 · 2018: 14 · 2017: 18 · 2016: 15 · 2015: 15 · 2014: 20 · 2013: 26 · 2012: 27
- FWCI
- 6.76
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Algorithms and Data Compression
- Keywords
- information retrieval evaluation, test collections, TREC, clefs, NTCIR, user evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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