{"id":"https://openalex.org/W6888491253","doi":"https://doi.org/10.21227/hg2s-f350","title":"Dataset on RAG Pipeline Evaluation for Retrieval and Generative Response Accuracy Testing","display_name":"Dataset on RAG Pipeline Evaluation for Retrieval and Generative Response Accuracy Testing","publication_year":2024,"publication_date":"2024-08-31","ids":{"openalex":"https://openalex.org/W6888491253","doi":"https://doi.org/10.21227/hg2s-f350"},"language":"en","primary_location":{"id":"doi:10.21227/hg2s-f350","is_oa":true,"landing_page_url":"https://doi.org/10.21227/hg2s-f350","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/hg2s-f350","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Venkatesh, Pruthvi Raj","orcid":null},"institutions":[{"id":"https://openalex.org/I121750182","display_name":"National Institute of Technology Warangal","ror":"https://ror.org/017ebfz38","country_code":"IN","type":"education","lineage":["https://openalex.org/I121750182"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Venkatesh, Pruthvi Raj","raw_affiliation_strings":["NIT Warangal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NIT Warangal","institution_ids":["https://openalex.org/I121750182"]}]},{"author_position":"last","author":{"id":null,"display_name":"P, Radha Krishna","orcid":null},"institutions":[{"id":"https://openalex.org/I121750182","display_name":"National Institute of Technology Warangal","ror":"https://ror.org/017ebfz38","country_code":"IN","type":"education","lineage":["https://openalex.org/I121750182"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"P, Radha Krishna","raw_affiliation_strings":["NIT Warangal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NIT Warangal","institution_ids":["https://openalex.org/I121750182"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I121750182"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7505999803543091},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6244999766349792},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6064000129699707},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.605400025844574},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5175999999046326},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.42489999532699585}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7662000060081482},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7505999803543091},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6244999766349792},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6064000129699707},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.605400025844574},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5175999999046326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4779999852180481},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4512999951839447},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4334000051021576},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.42489999532699585},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4203999936580658},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.38350000977516174},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3749000132083893},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3231000006198883},{"id":"https://openalex.org/C551230270","wikidata":"https://www.wikidata.org/wiki/Q4368942","display_name":"Data retrieval","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.3116999864578247},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.25760000944137573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/hg2s-f350","is_oa":true,"landing_page_url":"https://doi.org/10.21227/hg2s-f350","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.21227/hg2s-f350","is_oa":true,"landing_page_url":"https://doi.org/10.21227/hg2s-f350","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Dataset"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"dataset":[1,31,43,65,88],"has":[2],"been":[3],"meticulously":[4],"curated":[5],"to":[6],"evaluate":[7],"the":[8,58,63],"efficiency":[9],"of":[10,47,85],"Retrieval-Augmented":[11],"Generation":[12],"(RAG)":[13],"pipelines":[14],"in":[15,99,107],"both":[16],"retrieval":[17],"and":[18,38,70,81,103],"generative":[19],"accuracy,":[20],"with":[21,52],"a":[22,79,91],"particular":[23],"focus":[24],"on":[25],"scenarios":[26],"involving":[27],"overlapping":[28],"contexts.":[29],"The":[30,41],"comprises":[32],"two":[33],"primary":[34],"components:":[35],"Motor":[36,42],"data":[37,46,69],"Employee":[39,64],"data.":[40],"includes":[44],"master":[45,68],"various":[48],"motor":[49],"models":[50],"along":[51],"their":[53],"corresponding":[54],"manuals,":[55],"linked":[56,74],"by":[57,75],"motor's":[59],"model":[60],"name.":[61],"Similarly,":[62],"encompasses":[66],"employee":[67],"associated":[71],"policy":[72],"documents,":[73],"department.":[76],"By":[77],"providing":[78],"diverse":[80],"contextually":[82],"rich":[83],"set":[84],"information,":[86],"this":[87],"serves":[89],"as":[90],"comprehensive":[92],"resource":[93],"for":[94],"testing":[95],"RAG":[96],"pipelines'":[97],"capabilities":[98],"handling":[100],"complex":[101],"queries":[102],"generating":[104],"precise":[105],"responses":[106],"domains":[108],"where":[109],"context":[110],"overlap":[111],"is":[112],"prevalent.":[113]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}