{"id":"https://openalex.org/W7225198247","doi":"https://doi.org/10.48550/arxiv.2611.03525","title":"Grading the Unannotated: Insights from the WorkRB Skill Extraction Challenge at RecSys-HR 2026","display_name":"Grading the Unannotated: Insights from the WorkRB Skill Extraction Challenge at RecSys-HR 2026","publication_year":2026,"publication_date":"2026-11-02","ids":{"openalex":"https://openalex.org/W7225198247","doi":"https://doi.org/10.48550/arxiv.2611.03525"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2611.03525","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2611.03525","pdf_url":"https://arxiv.org/pdf/2611.03525","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2611.03525","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133790448","display_name":"Warre Veys","orcid":"https://orcid.org/0009-0000-6838-9954","observed_orcids":["https://orcid.org/0009-0000-6838-9954"]},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]},{"id":"https://openalex.org/I4210114974","display_name":"IMEC","ror":"https://ror.org/02kcbn207","country_code":"BE","type":"nonprofit","lineage":["https://openalex.org/I4210114974"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Veys, Warre","raw_affiliation_strings":["Ghent University -imec , 9052 Gent , Belgium","TechWolf , 9000 Gent , Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ghent University -imec , 9052 Gent , Belgium","institution_ids":["https://openalex.org/I32597200","https://openalex.org/I4210114974"]},{"raw_affiliation_string":"TechWolf , 9000 Gent , Belgium","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014844779","display_name":"Luis Gasc\u00f3","orcid":"https://orcid.org/0000-0002-4976-9879","observed_orcids":["https://orcid.org/0000-0002-4976-9879"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gasco, Luis","raw_affiliation_strings":["Avature , Barcelona , Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Avature , Barcelona , Spain","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012730698","display_name":"Jens-Joris Decorte","orcid":"https://orcid.org/0009-0000-7892-1211","observed_orcids":["https://orcid.org/0009-0000-7892-1211"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Decorte, Jens-Joris","raw_affiliation_strings":["TechWolf , 9000 Gent , Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TechWolf , 9000 Gent , Belgium","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.4106000065803528,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.4106000065803528,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.3337000012397766,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13812","display_name":"AI and HR Technologies","score":0.15389999747276306,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/skill-extraction","display_name":"skill extraction","score":0.8930000066757202},{"id":"https://openalex.org/keywords/skill-recommendation","display_name":"skill recommendation","score":0.7239999771118164},{"id":"https://openalex.org/keywords/benchmark-dataset","display_name":"benchmark dataset","score":0.3529999852180481},{"id":"https://openalex.org/keywords/semantic-understanding","display_name":"semantic understanding","score":0.4699999988079071},{"id":"https://openalex.org/keywords/annotation-methods","display_name":"annotation methodology","score":0.4189999997615814}],"concepts":[{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6193000078201294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6015999913215637},{"id":"https://openalex.org/C2777413886","wikidata":"https://www.wikidata.org/wiki/Q3276013","display_name":"Fluency","level":2,"score":0.5807999968528748},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.5467000007629395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5411999821662903},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5224000215530396},{"id":"https://openalex.org/C2777286243","wikidata":"https://www.wikidata.org/wiki/Q5591926","display_name":"Grading (engineering)","level":2,"score":0.4957999885082245},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4846999943256378},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.482699990272522},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4081000089645386},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39879998564720154},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.3874000012874603},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.3869999945163727},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.38659998774528503},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3804999887943268},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.34790000319480896},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C169345407","wikidata":"https://www.wikidata.org/wiki/Q8216221","display_name":"Uncorrelated","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.26579999923706055},{"id":"https://openalex.org/C111640148","wikidata":"https://www.wikidata.org/wiki/Q847349","display_name":"Rubric","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C37381756","wikidata":"https://www.wikidata.org/wiki/Q20203288","display_name":"Representativeness heuristic","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2529999911785126},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2611.03525","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2611.03525","pdf_url":"https://arxiv.org/pdf/2611.03525","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2611.03525","doi":"https://doi.org/10.48550/arxiv.2611.03525","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2611.03525","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2611.03525","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2611.03525","pdf_url":"https://arxiv.org/pdf/2611.03525","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225198247.pdf","grobid_xml":"https://content.openalex.org/works/W7225198247.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7214277921","https://openalex.org/W7170079924","https://openalex.org/W4403964501","https://openalex.org/W7223986442","https://openalex.org/W7163374377","https://openalex.org/W7218791384","https://openalex.org/W4411814419","https://openalex.org/W7170194685","https://openalex.org/W3117329469","https://openalex.org/W4407208345"],"abstract_inverted_index":{"Skill":[0],"extraction":[1],"benchmarks":[2],"score":[3],"models":[4,33],"against":[5,79],"a":[6,64,69,95,109,113],"few":[7],"human-annotated":[8],"positives":[9],"and":[10,44,74,127,161,216,222],"treat":[11],"every":[12,60,240],"other":[13],"skill":[14,62,152,226],"in":[15,63],"the":[16,22,36,41,75,120,133,136,155,158,164,181,190,193,195,257],"vocabulary":[17],"as":[18,146],"equally":[19],"wrong.":[20],"At":[21],"scale":[23],"of":[24,97,108,138,157,180,192],"ESCO":[25],"(13,939":[26],"skills)":[27],"this":[28],"binary":[29,99,202,219],"view":[30],"can":[31,117],"reward":[32],"that":[34,112,224],"rank":[35],"annotated":[37],"positive":[38],"high":[39],"for":[40,88],"wrong":[42],"reason,":[43],"hides":[45],"semantically":[46],"nonsensical":[47],"predictions.":[48],"The":[49,166,232],"WorkRB":[50],"Challenge":[51],"at":[52,189,247],"RecSys-HR":[53],"2026":[54],"replaces":[55],"it":[56,259],"with":[57,124,143,149,200],"graded":[58,196],"relevance:":[59],"candidate":[61],"model's":[65],"ranking":[66,182],"window":[67,183],"carries":[68],"0":[70],"to":[71],"4":[72],"grade,":[73],"leaderboard":[76],"scores":[77],"nDCG@100":[78],"these":[80],"grades.":[81],"Since":[82],"ternary":[83],"judgements":[84],"proved":[85],"too":[86],"coarse":[87],"objective":[89],"annotation,":[90],"relevance":[91],"is":[92,198,228,236],"decomposed":[93],"into":[94],"cascade":[96],"four":[98],"questions":[100],"(same":[101],"domain,":[102],"activity":[103],"mentioned,":[104],"right":[105],"granularity,":[106],"replacement":[107],"gold":[110],"positive)":[111],"smaller":[114],"LLM":[115,159],"judge":[116],"answer":[118],"reliably;":[119],"labels":[121,208],"are":[122],"validated":[123],"blind":[125],"human":[126],"frontier-model":[128],"re-annotation.":[129],"This":[130],"paper":[131],"documents":[132],"annotation":[134],"methodology,":[135],"construction":[137],"five":[139],"benchmark":[140],"datasets":[141],"(four":[142],"vacancy":[144],"sentences":[145],"queries,":[147],"one":[148],"lexically":[150],"distant":[151],"surface":[153],"forms),":[154],"validation":[156],"cascade,":[160],"insights":[162],"from":[163,176],"submissions.":[165],"field":[167],"converged":[168],"on":[169],"multi-stage":[170],"retrieve-and-rerank":[171],"pipelines,":[172],"whose":[173],"gains":[174],"come":[175],"clearing":[177],"nonsense":[178],"out":[179],"rather":[184],"than":[185,218,256],"finding":[186],"more":[187,254],"positives:":[188],"top":[191,214],"leaderboard,":[194],"ordering":[197],"uncorrelated":[199],"what":[201],"evaluation":[203,220],"would":[204],"have":[205],"said.":[206],"Graded":[207],"expose":[209],"much":[210],"bigger":[211],"gaps":[212],"between":[213],"submissions":[215],"baselines":[217,258],"does,":[221],"show":[223],"semantic-understanding":[225],"recommendation":[227],"still":[229],"wide":[230],"open.":[231],"clear":[233],"next":[234],"direction":[235],"bringing":[237],"efficiency":[238],"back:":[239],"submitted":[241],"system":[242],"we":[243],"could":[244],"size":[245],"uses":[246],"least":[248],"42x,":[249],"typically":[250],"several":[251],"hundred":[252],"times,":[253],"parameters":[255],"beats.":[260]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2026-11-04T00:00:00"}