{"id":"https://openalex.org/W4417070856","doi":"https://doi.org/10.48550/arxiv.2509.08808","title":"Handling Open-Vocabulary Constructs in Formalizing Specifications: Retrieval-Augmented Parsing with Expert Knowledge","display_name":"Handling Open-Vocabulary Constructs in Formalizing Specifications: Retrieval-Augmented Parsing with Expert Knowledge","publication_year":2025,"publication_date":"2025-09-10","ids":{"openalex":"https://openalex.org/W4417070856","doi":"https://doi.org/10.48550/arxiv.2509.08808"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.08808","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.08808","pdf_url":"https://arxiv.org/pdf/2509.08808","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,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.08808","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Hasan, Mohammad Saqib","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hasan, Mohammad Saqib","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030511963","display_name":"Sayontan Ghosh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ghosh, Sayontan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045730504","display_name":"Dhruv Verma","orcid":"https://orcid.org/0000-0002-4780-5901"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Verma, Dhruv","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056117639","display_name":"Geoff Kuenning","orcid":"https://orcid.org/0000-0002-3882-2072"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kuenning, Geoff","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064923375","display_name":"Erez Zadok","orcid":"https://orcid.org/0000-0001-5248-9184"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zadok, Erez","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039202410","display_name":"Scott A. Smolka","orcid":"https://orcid.org/0000-0002-7348-630X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Smolka, Scott A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101768349","display_name":"Niranjan Balasubramanian","orcid":"https://orcid.org/0000-0003-4187-9368"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Balasubramanian, Niranjan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.7871999740600586,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10181","display_name":"Natural Language Processing Techniques","score":0.7871999740600586,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10028","display_name":"Topic Modeling","score":0.1152999997138977,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13629","display_name":"Text Readability and Simplification","score":0.01759999990463257,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5472999811172485},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.47850000858306885},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4652999937534332},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.4514000117778778},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4426000118255615},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.4377000033855438},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4097999930381775},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.388700008392334},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.36959999799728394}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8637999892234802},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6485999822616577},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5472999811172485},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.47850000858306885},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4593000113964081},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.4514000117778778},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44620001316070557},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4426000118255615},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.4377000033855438},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4097999930381775},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.388700008392334},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.36959999799728394},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3659999966621399},{"id":"https://openalex.org/C105002631","wikidata":"https://www.wikidata.org/wiki/Q4833645","display_name":"Subject-matter expert","level":3,"score":0.3547999858856201},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.33970001339912415},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.33550000190734863},{"id":"https://openalex.org/C2777220311","wikidata":"https://www.wikidata.org/wiki/Q6423340","display_name":"Knowledge acquisition","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C102600418","wikidata":"https://www.wikidata.org/wiki/Q6517507","display_name":"Legal expert system","level":3,"score":0.31139999628067017},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C2778121359","wikidata":"https://www.wikidata.org/wiki/Q8096","display_name":"Lexicon","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C84685590","wikidata":"https://www.wikidata.org/wiki/Q1540472","display_name":"Knowledge engineering","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.2614000141620636}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2509.08808","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.08808","pdf_url":"https://arxiv.org/pdf/2509.08808","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,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.08808","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.08808","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,"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":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.08808","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.08808","pdf_url":"https://arxiv.org/pdf/2509.08808","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,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,81,112,153],"study":[1],"the":[2,14,79,90,93,134,139,159,172,189,204,209,232],"problem":[3,206],"of":[4,16,39,192,234],"Open-Vocabulary":[5],"Constructs(OVCs)":[6],"--":[7,12],"ones":[8],"not":[9],"known":[10],"beforehand":[11],"in":[13,87,146],"context":[15],"converting":[17],"natural":[18],"language":[19],"(NL)":[20],"specifications":[21],"into":[22],"formal":[23],"languages":[24],"(e.g.,":[25],"temporal":[26],"logic":[27],"or":[28,61],"code).":[29],"Models":[30],"fare":[31],"poorly":[32],"on":[33,58,163,188],"OVCs":[34],"due":[35],"to":[36,67,89,130,137,170,183,186],"a":[37,42,47,101,115,126,198,225],"lack":[38],"necessary":[40],"knowledge":[41,73,99,241],"priori.":[43],"In":[44],"such":[45],"situations,":[46],"domain":[48,62],"expert":[49,98,240],"can":[50],"provide":[51],"correct":[52,109,140],"constructs":[53],"at":[54],"inference":[55],"time":[56],"based":[57],"their":[59],"preferences":[60],"knowledge.":[63,194],"Our":[64,219],"goal":[65],"is":[66,224],"effectively":[68],"reuse":[69],"this":[70,121,150],"inference-time,":[71],"expert-provided":[72],"for":[74,149],"future":[75],"parses":[76],"without":[77],"retraining":[78],"model.":[80],"present":[82],"dynamic":[83,239],"knowledge-augmented":[84],"parsing(DKAP),":[85],"where":[86],"addition":[88],"input":[91],"sentence,":[92,166],"model":[94],"receives":[95],"(dynamically":[96],"growing)":[97],"as":[100],"key-value":[102,135],"lexicon":[103],"that":[104,119,222],"associates":[105],"NL":[106],"phrases":[107],"with":[108],"OVC":[110],"constructs.":[111],"propose":[113,180],"ROLex,":[114],"retrieval-augmented":[116,151],"parsing":[117],"approach":[118],"uses":[120],"lexicon.":[122],"A":[123,142],"retriever":[124],"and":[125,132,158,207,217,228],"generator":[127],"are":[128],"trained":[129],"find":[131],"use":[133],"store":[136],"produce":[138],"parse.":[141],"key":[143],"challenge":[144],"lies":[145],"curating":[147],"data":[148,156,160],"parser.":[152,174],"utilize":[154],"synthetic":[155],"generation":[157],"augmentation":[161],"techniques":[162],"annotated":[164],"(NL":[165],"FL":[167],"statement)":[168],"pairs":[169],"train":[171],"augmented":[173],"To":[175],"improve":[176,231],"training":[177],"effectiveness,":[178],"we":[179,196],"multiple":[181],"strategies":[182],"teach":[184],"models":[185,236],"focus":[187],"relevant":[190],"subset":[191],"retrieved":[193],"Finally,":[195],"introduce":[197],"new":[199],"evaluation":[200],"paradigm":[201],"modeled":[202],"after":[203],"DKAP":[205,223],"simulate":[208],"scenario":[210],"across":[211],"three":[212],"formalization":[213],"tasks":[214],"(NL2LTL,":[215],"NL2Code,":[216],"NL2CMD).":[218],"evaluations":[220],"show":[221],"difficult":[226],"challenge,":[227],"ROLex":[229],"helps":[230],"performance":[233],"baseline":[235],"by":[237],"using":[238],"effectively.":[242]},"counts_by_year":[],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}