{"id":"https://openalex.org/W4412945664","doi":"https://doi.org/10.18653/v1/2025.acl-long.386","title":"Large Language Models are Good Relational Learners","display_name":"Large Language Models are Good Relational Learners","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412945664","doi":"https://doi.org/10.18653/v1/2025.acl-long.386"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.acl-long.386","doi":"https://doi.org/10.18653/v1/2025.acl-long.386","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.386","pdf_url":"https://aclanthology.org/2025.acl-long.386.pdf","source":{"id":"https://openalex.org/S4393915740","display_name":"Proceedings of the conference - Association for Computational Linguistics. Meeting","issn_l":"0736-587X","issn":["0736-587X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://aclanthology.org/2025.acl-long.386.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125554383","display_name":"Fang Wu","orcid":"https://orcid.org/0000-0001-7240-3915","observed_orcids":["https://orcid.org/0000-0001-7240-3915"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fang Wu","raw_affiliation_strings":["Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062564046","display_name":"Vijay Prakash Dwivedi","orcid":"https://orcid.org/0000-0003-1836-4323","observed_orcids":["https://orcid.org/0000-0003-1836-4323"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vijay Prakash Dwivedi","raw_affiliation_strings":["Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091272738","display_name":"Jure Leskovec","orcid":"https://orcid.org/0000-0002-5411-923X","observed_orcids":["https://orcid.org/0000-0002-5411-923X"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jure Leskovec","raw_affiliation_strings":["Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":3.7985,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.93856895,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"7835","last_page":"7854"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.8817999958992004,"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":0.8817999958992004,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.033799998462200165,"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":0.01899999938905239,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.9850000143051147},{"id":"https://openalex.org/keywords/relational-learning","display_name":"relational learning","score":0.9399999976158142},{"id":"https://openalex.org/keywords/relational-reasoning","display_name":"relational reasoning","score":0.49799999594688416},{"id":"https://openalex.org/keywords/graph-reasoning","display_name":"graph reasoning","score":0.32499998807907104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.784076452255249},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5624539256095886},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42407143115997314},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.36623305082321167}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/2025.acl-long.386","doi":"https://doi.org/10.18653/v1/2025.acl-long.386","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.386","pdf_url":"https://aclanthology.org/2025.acl-long.386.pdf","source":{"id":"https://openalex.org/S4393915740","display_name":"Proceedings of the conference - Association for Computational Linguistics. Meeting","issn_l":"0736-587X","issn":["0736-587X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2506.05725","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.05725","pdf_url":"https://arxiv.org/pdf/2506.05725","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2506.05725","doi":"https://doi.org/10.48550/arxiv.2506.05725","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.05725","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":"doi:10.18653/v1/2025.acl-long.386","doi":"https://doi.org/10.18653/v1/2025.acl-long.386","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.386","pdf_url":"https://aclanthology.org/2025.acl-long.386.pdf","source":{"id":"https://openalex.org/S4393915740","display_name":"Proceedings of the conference - Association for Computational Linguistics. Meeting","issn_l":"0736-587X","issn":["0736-587X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412945664.pdf","grobid_xml":"https://content.openalex.org/works/W4412945664.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7196965405","https://openalex.org/W7161582021","https://openalex.org/W4414601276","https://openalex.org/W4412875489","https://openalex.org/W7131297933","https://openalex.org/W7129028100","https://openalex.org/W4416501564","https://openalex.org/W4417093508","https://openalex.org/W4407359586","https://openalex.org/W4415798762"],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2],"(LLMs)":[3],"have":[4],"demonstrated":[5],"remarkable":[6],"capabilities":[7],"across":[8],"various":[9],"domains,":[10],"yet":[11],"their":[12],"application":[13],"to":[14,73,102,121,147,171],"relational":[15,26,48,76,95,150],"deep":[16],"learning":[17],"(RDL)":[18],"remains":[19],"underexplored.Existing":[20],"approaches":[21],"adapt":[22],"LLMs":[23,79,101,173],"by":[24],"traversing":[25],"links":[27],"between":[28],"entities":[29],"in":[30],"a":[31,61,66,81,115,140,166],"database":[32],"and":[33,52,105,130,168],"converting":[34],"the":[35,93,111,145],"structured":[36,75,137,175],"data":[37,176],"into":[38,136],"flat":[39],"text":[40],"documents,":[41],"but":[42],"this":[43],"text-based":[44,87],"serialization":[45,88],"disregards":[46],"critical":[47],"structures,":[49],"introduces":[50],"redundancy,":[51],"often":[53],"exceeds":[54],"standard":[55],"LLM":[56,146],"context":[57],"lengths.We":[58],"introduce":[59],"Rel-LLM,":[60],"novel":[62],"architecture":[63],"that":[64,125,156],"employs":[65],"graph":[67],"neural":[68],"network":[69],"(GNN)":[70],"based":[71],"encoder":[72,113],"create":[74],"prompts":[77,138],"for":[78],"within":[80],"retrieval-augmented":[82],"generation":[83],"(RAG)":[84],"framework.Unlike":[85],"traditional":[86],"approaches,":[89],"our":[90],"method":[91],"preserves":[92],"inherent":[94],"structure":[96],"of":[97],"databases":[98],"while":[99],"enabling":[100],"effectively":[103,143],"process":[104],"reason":[106,148],"over":[107,149],"complex":[108],"entity":[109,120,128],"relationships.Specifically,":[110],"GNN":[112],"extracts":[114],"local":[116],"subgraph":[117],"around":[118],"an":[119],"build":[122],"feature":[123],"representations":[124,133],"contain":[126],"relevant":[127],"relationships":[129],"temporal":[131],"dependencies.These":[132],"are":[134],"transformed":[135],"using":[139],"denormalization":[141],"process,":[142],"allowing":[144],"structures.Through":[151],"extensive":[152],"experiments,":[153],"we":[154],"demonstrate":[155],"Rel-LLM":[157],"outperforms":[158],"existing":[159],"methods":[160],"on":[161],"key":[162],"RDL":[163],"tasks,":[164],"offering":[165],"scalable":[167],"efficient":[169],"approach":[170],"integrating":[172],"with":[174],"sources.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":4}],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2025-10-10T00:00:00"}
