{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-gold-standard-dataset-for-the-reviewer","title":"A Gold Standard Dataset for the Reviewer Assignment Problem","arxiv_id":"2303.16750","date":"2023-03-23","proceeding":null,"authors":["Ivan Stelmakh","John Wieting","Sarina Xi","Graham Neubig","Nihar B. Shah"],"abstract":"Many peer-review venues are using algorithms to assign submissions to reviewers. The crux of such automated approaches is the notion of the \"similarity score\" -- a numerical estimate of the expertise of a reviewer in reviewing a paper -- and many algorithms have been proposed to compute these scores. However, these algorithms have not been subjected to a principled comparison, making it difficult for stakeholders to choose the algorithm in an evidence-based manner. The key challenge in comparing existing algorithms and developing better algorithms is the lack of publicly available gold-standard data. We address this challenge by collecting a novel dataset of similarity scores that we release to the research community. Our dataset consists of 477 self-reported expertise scores provided by 58 researchers who evaluated their expertise in reviewing papers they have read previously. Using our dataset, we compare several widely used similarity algorithms and offer key insights. First, all algorithms exhibit significant error, with misranking rates between 12%-30% in easier cases and 36%-43% in harder ones. Second, most specialized algorithms are designed to work with titles and abstracts of papers, and in this regime the SPECTER2 algorithm performs best. Interestingly, classical TF-IDF matches SPECTER2 in accuracy when given access to full submission texts. In contrast, off-the-shelf LLMs lag behind specialized approaches.","url_abs":"https://arxiv.org/abs/2303.16750v2","url_pdf":"https://arxiv.org/pdf/2303.16750v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-gold-standard-dataset-for-the-reviewer","repo_url":"https://github.com/niharshah/goldstandard-reviewer-paper-match","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-gold-standard-dataset-for-the-reviewer","repo_url":"https://github.com/openreview/openreview-expertise","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.16750","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.16750"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/niharshah/goldstandard-reviewer-paper-match","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/openreview/openreview-expertise","reach":null}],"summary":{"ran_draft_wrong":2,"unverified":4},"by_repo_kind":{"official":{"samples":6,"ran":2,"repositories":2}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"617b04c971f6b909","entry":"counter_to_tensor","repo":"openreview/openreview-expertise","repo_kind":"official","path":"expertise/models/multifacet_recommender/multifacet_recommender.py","file_url":"https://github.com/openreview/openreview-expertise/blob/HEAD/expertise/models/multifacet_recommender/multifacet_recommender.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"617b04c971f6b909"}},{"code_sha256_prefix":"313243ce5a696a04","entry":"load_ext_emb","repo":"openreview/openreview-expertise","repo_kind":"official","path":"expertise/models/multifacet_recommender/multifacet_recommender.py","file_url":"https://github.com/openreview/openreview-expertise/blob/HEAD/expertise/models/multifacet_recommender/multifacet_recommender.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"313243ce5a696a04"}},{"code_sha256_prefix":"01bb65aaec790f1a","entry":"parse_args","repo":"niharshah/goldstandard-reviewer-paper-match","repo_kind":"official","path":"evaluation_script.py","file_url":"https://github.com/niharshah/goldstandard-reviewer-paper-match/blob/HEAD/evaluation_script.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"01bb65aaec790f1a"}},{"code_sha256_prefix":"e77989bd513b9836","entry":"parse_args","repo":"niharshah/goldstandard-reviewer-paper-match","repo_kind":"official","path":"scripts/prepare_dataset.py","file_url":"https://github.com/niharshah/goldstandard-reviewer-paper-match/blob/HEAD/scripts/prepare_dataset.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e77989bd513b9836"}},{"code_sha256_prefix":"05393c48abdec847","entry":"prepare_dataset","repo":"niharshah/goldstandard-reviewer-paper-match","repo_kind":"official","path":"scripts/prepare_dataset.py","file_url":"https://github.com/niharshah/goldstandard-reviewer-paper-match/blob/HEAD/scripts/prepare_dataset.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"05393c48abdec847"}},{"code_sha256_prefix":"d5b6846d33c4a8bd","entry":"squeeze_into_tensors","repo":"openreview/openreview-expertise","repo_kind":"official","path":"expertise/models/multifacet_recommender/multifacet_recommender.py","file_url":"https://github.com/openreview/openreview-expertise/blob/HEAD/expertise/models/multifacet_recommender/multifacet_recommender.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d5b6846d33c4a8bd"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}