{"id":"https://openalex.org/W4417266741","doi":"https://doi.org/10.48550/arxiv.2507.19586","title":"Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning","display_name":"Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning","publication_year":2025,"publication_date":"2025-07-25","ids":{"openalex":"https://openalex.org/W4417266741","doi":"https://doi.org/10.48550/arxiv.2507.19586"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2507.19586","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.19586","pdf_url":"https://arxiv.org/pdf/2507.19586","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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/2507.19586","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071091495","display_name":"Shengyuan Wang","orcid":"https://orcid.org/0000-0002-9264-4860"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wang, Shengyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101709392","display_name":"Jie Feng","orcid":"https://orcid.org/0000-0002-5474-3286"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Jie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101464359","display_name":"Tianhui Liu","orcid":"https://orcid.org/0000-0001-6011-9263"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Tianhui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046419834","display_name":"Dan Pei","orcid":"https://orcid.org/0000-0002-5113-838X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pei, Dan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100355339","display_name":"Yong Li","orcid":"https://orcid.org/0000-0002-1183-5359"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yong","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5071091495"],"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.1680999994277954,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.1680999994277954,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.1006999984383583,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10757","display_name":"Geographic Information Systems Studies","score":0.09040000289678574,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.972100019454956},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.5746999979019165},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5217000246047974},{"id":"https://openalex.org/keywords/geospatial-pdf","display_name":"Geospatial PDF","score":0.4138000011444092},{"id":"https://openalex.org/keywords/geographic-information-system","display_name":"Geographic information system","score":0.335999995470047}],"concepts":[{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.972100019454956},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.5746999979019165},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5637999773025513},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5217000246047974},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4235000014305115},{"id":"https://openalex.org/C130745260","wikidata":"https://www.wikidata.org/wiki/Q5548542","display_name":"Geospatial PDF","level":3,"score":0.4138000011444092},{"id":"https://openalex.org/C41856607","wikidata":"https://www.wikidata.org/wiki/Q483130","display_name":"Geographic information system","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3012999892234802},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.25029999017715454},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.2468000054359436}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2507.19586","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.19586","pdf_url":"https://arxiv.org/pdf/2507.19586","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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.2507.19586","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.19586","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2507.19586","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.19586","pdf_url":"https://arxiv.org/pdf/2507.19586","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2],"(LLMs)":[3],"possess":[4],"extensive":[5,96],"world":[6],"knowledge,":[7,10,34],"including":[8],"geospatial":[9,18,33,37,44,69,85,89,108,128,163],"which":[11],"has":[12,59],"been":[13,60],"successfully":[14],"applied":[15],"to":[16,36,126,133],"various":[17],"tasks":[19],"such":[20],"as":[21],"mobility":[22],"prediction":[23],"and":[24,66,153,165],"social":[25],"indicator":[26],"prediction.":[27],"However,":[28],"LLMs":[29,58,161],"often":[30],"generate":[31],"inaccurate":[32],"leading":[35,132],"hallucinations":[38,70,105,129],"(incorrect":[39],"or":[40],"inconsistent":[41],"representations":[42],"of":[43,53,68,137,150,160],"information)":[45],"that":[46],"compromise":[47],"their":[48,107],"reliability.":[49],"While":[50],"the":[51,63,104,141,148,158],"phenomenon":[52],"general":[54],"knowledge":[55,90,164],"hallucination":[56],"in":[57,106,130,156,162],"widely":[61],"studied,":[62],"systematic":[64],"evaluation":[65,82,97],"mitigation":[67],"remain":[71],"largely":[72],"unexplored.":[73],"To":[74],"address":[75],"this":[76],"gap,":[77],"we":[78,102,114],"propose":[79],"a":[80,116,134],"comprehensive":[81],"framework":[83],"for":[84,92],"hallucinations,":[86],"leveraging":[87],"structured":[88],"graphs":[91],"controlled":[93],"assessment.":[94],"Through":[95],"across":[98],"20":[99],"advanced":[100],"LLMs,":[101,131],"uncover":[103],"knowledge.":[109],"Building":[110],"on":[111,122,140],"these":[112],"insights,":[113],"introduce":[115],"dynamic":[117],"factuality":[118],"aligning":[119],"method":[120],"based":[121],"Kahneman-Tversky":[123],"Optimization":[124],"(KTO)":[125],"mitigate":[127],"performance":[135],"improvement":[136],"over":[138],"29.6%":[139],"proposed":[142],"benchmark.":[143],"Extensive":[144],"experimental":[145],"results":[146],"demonstrate":[147],"effectiveness":[149],"our":[151],"benchmark":[152],"learning":[154],"algorithm":[155],"enhancing":[157],"trustworthiness":[159],"reasoning":[166],"tasks.":[167]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}
