{"id":"https://openalex.org/W7166802848","doi":"https://doi.org/10.18653/v1/2026.findings-acl.561","title":"Follow the Path: Reasoning over Knowledge Graph Paths to Improve Large Language Model Factuality","display_name":"Follow the Path: Reasoning over Knowledge Graph Paths to Improve Large Language Model Factuality","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166802848","doi":"https://doi.org/10.18653/v1/2026.findings-acl.561"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.561","doi":"https://doi.org/10.18653/v1/2026.findings-acl.561","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.561","pdf_url":"https://aclanthology.org/2026.findings-acl.561.pdf","source":{"id":"https://openalex.org/S4363605144","display_name":"Findings of the Association for Computational Linguistics: ACL (Findings of ACL)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://aclanthology.org/2026.findings-acl.561.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139749467","display_name":"Mike Zhang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I124055696","display_name":"University of Copenhagen","ror":"https://ror.org/035b05819","country_code":"DK","type":"education","lineage":["https://openalex.org/I124055696"]},{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Mike Zhang","raw_affiliation_strings":["Aalborg University Pioneer Centre for Artificial Intelligence","University of Copenhagen"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalborg University Pioneer Centre for Artificial Intelligence","institution_ids":["https://openalex.org/I891191580"]},{"raw_affiliation_string":"University of Copenhagen","institution_ids":["https://openalex.org/I124055696"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013472329","display_name":"Johannes Bjerva","orcid":"https://orcid.org/0000-0002-9512-0739","observed_orcids":["https://orcid.org/0000-0002-9512-0739"]},"institutions":[{"id":"https://openalex.org/I124055696","display_name":"University of Copenhagen","ror":"https://ror.org/035b05819","country_code":"DK","type":"education","lineage":["https://openalex.org/I124055696"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Johannes Bjerva","raw_affiliation_strings":["University of Copenhagen"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Copenhagen","institution_ids":["https://openalex.org/I124055696"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5128852847","display_name":"Russa Biswas","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I124055696","display_name":"University of Copenhagen","ror":"https://ror.org/035b05819","country_code":"DK","type":"education","lineage":["https://openalex.org/I124055696"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Russa Biswas","raw_affiliation_strings":["University of Copenhagen"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Copenhagen","institution_ids":["https://openalex.org/I124055696"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.45295025,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"11574","last_page":"11590"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9452999830245972,"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/T10028","display_name":"Topic Modeling","score":0.9452999830245972,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.025599999353289604,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10181","display_name":"Natural Language Processing Techniques","score":0.007400000002235174,"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/factuality","display_name":"factuality","score":0.6639999747276306},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.8259999752044678},{"id":"https://openalex.org/keywords/reasoning-traces","display_name":"reasoning traces","score":0.9089999794960022},{"id":"https://openalex.org/keywords/open-domain-question-answering","display_name":"open-domain question answering","score":0.8550000190734863},{"id":"https://openalex.org/keywords/instruction-tuned-llms","display_name":"instruction-tuned LLMs","score":0.6669999957084656},{"id":"https://openalex.org/keywords/fine-tuning","display_name":"fine-tuning","score":0.6639999747276306},{"id":"https://openalex.org/keywords/multi-hop-reasoning","display_name":"multi-hop reasoning","score":0.6159999966621399}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7019000053405762},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5756000280380249},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5612999796867371},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47749999165534973},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4657000005245209},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.38600000739097595},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.37599998712539673},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.2637999951839447},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.25999999046325684},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.561","doi":"https://doi.org/10.18653/v1/2026.findings-acl.561","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.561","pdf_url":"https://aclanthology.org/2026.findings-acl.561.pdf","source":{"id":"https://openalex.org/S4363605144","display_name":"Findings of the Association for Computational Linguistics: ACL (Findings of ACL)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:openaire/ffca2dad-3202-4af1-a867-cd0024af81c2","doi":null,"is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/ffca2dad-3202-4af1-a867-cd0024af81c2","pdf_url":"https://vbn.aau.dk/ws/files/856900264/2026.findings-acl.561.pdf","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"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":"Zhang, M, Bjerva, J & Biswas, R 2026, Follow the Path: Reasoning over Knowledge Graph Paths to Improve Large Language Model Factuality. in Findings of the Association for Computational Linguistics: ACL 2026. Association for Computational Linguistics (ACL), pp. 11574-11590. https://doi.org/10.18653/v1/2026.findings-acl.561","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-acl.561","doi":"https://doi.org/10.18653/v1/2026.findings-acl.561","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.561","pdf_url":"https://aclanthology.org/2026.findings-acl.561.pdf","source":{"id":"https://openalex.org/S4363605144","display_name":"Findings of the Association for Computational Linguistics: ACL (Findings of ACL)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G6736582538","display_name":null,"funder_award_id":"VIL57392","funder_id":"https://openalex.org/F4320310490","funder_display_name":"Villum Fonden"}],"funders":[{"id":"https://openalex.org/F1100377486","display_name":"European High Performance Computing Joint Undertaking","ror":"https://ror.org/04dvh0v63"},{"id":"https://openalex.org/F4320310490","display_name":"Villum Fonden","ror":"https://ror.org/007ww2d15"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320322975","display_name":"Danish e-Infrastructure Cooperation","ror":"https://ror.org/03ge1nb22"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166802848.pdf","grobid_xml":"https://content.openalex.org/works/W7166802848.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4412888523","https://openalex.org/W4399026201","https://openalex.org/W4409670780","https://openalex.org/W7166507191","https://openalex.org/W4394906217","https://openalex.org/W4407759000","https://openalex.org/W7125612734","https://openalex.org/W7155038440","https://openalex.org/W7122606446","https://openalex.org/W4401024819"],"abstract_inverted_index":{"We":[0],"introduce":[1],"fs1,":[2],"a":[3,142],"simple":[4],"yet":[5],"effective":[6],"method":[7],"that":[8,59,78,109,134],"improves":[9,81],"the":[10,102,113,119,126],"factuality":[11],"of":[12,121],"reasoning":[13,20,41,122,136],"traces":[14,42,123],"by":[15,70],"collecting":[16],"them":[17,24,46],"from":[18],"large":[19],"models":[21],"and":[22,43,96],"grounding":[23],"in":[25,104,125,145],"knowledge":[26],"graph":[27],"(KG)":[28],"paths.We":[29],"fine-tune":[30],"eight":[31],"instructiontuned":[32],"Large":[33],"Language":[34],"Models":[35],"(LLMs)":[36],"on":[37,47,93],"3.9K":[38],"factually":[39],"grounded":[40],"rigorously":[44],"evaluate":[45],"six":[48],"complex":[49,86],"opendomain":[50],"question-answering":[51],"(QA)":[52],"benchmarks":[53],"encompassing":[54],"23.9K":[55],"questions.Our":[56],"results":[57],"demonstrate":[58,118],"our":[60,129],"fs1-tuned":[61],"model":[62,82],"consistently":[63],"outperforms":[64],"instruction-tuned":[65],"counterparts":[66],"with":[67],"parallel":[68],"sampling":[69],"6-14":[71],"absolute":[72],"points":[73],"(pass@16).Our":[74],"detailed":[75],"analysis":[76],"shows":[77,131],"fs1":[79],"considerably":[80],"performance":[83],"over":[84],"more":[85,91],"questions":[87],"(requiring":[88],"3":[89],"or":[90],"hops":[92],"KG":[94,139],"paths)":[95],"numerical":[97],"answer":[98],"types":[99],"compared":[100],"to":[101,137],"baselines.Furthermore,":[103],"single-pass":[105],"inference,":[106],"we":[107],"notice":[108],"smaller":[110],"LLMs":[111,147],"show":[112],"most":[114],"improvements.While":[115],"prior":[116],"works":[117],"effectiveness":[120],"primarily":[124],"STEM":[127],"domains,":[128],"work":[130],"strong":[132],"evidence":[133],"anchoring":[135],"factual":[138],"paths":[140],"is":[141],"critical":[143],"step":[144],"transforming":[146],"for":[148],"reliable":[149],"knowledge-intensive":[150],"tasks.":[151]},"counts_by_year":[],"updated_date":"2026-10-26T11:51:55.731654","created_date":"2026-07-02T00:00:00"}