{"id":"https://openalex.org/W4404792950","doi":"https://doi.org/10.18653/v1/2024.emnlp-main.743","title":"1+1&gt;2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?","display_name":"1+1&gt;2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4404792950","doi":"https://doi.org/10.18653/v1/2024.emnlp-main.743"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2024.emnlp-main.743","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.emnlp-main.743","pdf_url":"https://aclanthology.org/2024.emnlp-main.743.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2024.emnlp-main.743.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061883661","display_name":"Yue Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yue Huang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104334497","display_name":"Chenrui Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chenrui Fan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017943466","display_name":"Yuan-Fang Li","orcid":"https://orcid.org/0000-0003-4651-2821"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan Li","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002263051","display_name":"Siyuan Wu","orcid":"https://orcid.org/0000-0002-4826-1010"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siyuan Wu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039076312","display_name":"Tianyi Zhou","orcid":"https://orcid.org/0000-0001-5348-0632"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tianyi Zhou","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000755750","display_name":"Xiangliang Zhang","orcid":"https://orcid.org/0000-0002-3574-5665"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiangliang Zhang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5015105117","display_name":"Lichao Sun","orcid":"https://orcid.org/0000-0003-1539-7939"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lichao Sun","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5061883661"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3592,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.69419231,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"13394","last_page":"13412"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.978600025177002,"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":0.978600025177002,"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":0.9778000116348267,"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/computer-science","display_name":"Computer science","score":0.7786514759063721},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3708324432373047},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32278692722320557}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7786514759063721},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3708324432373047},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32278692722320557}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2024.emnlp-main.743","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.emnlp-main.743","pdf_url":"https://aclanthology.org/2024.emnlp-main.743.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2024.emnlp-main.743","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.emnlp-main.743","pdf_url":"https://aclanthology.org/2024.emnlp-main.743.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4404792950.pdf","grobid_xml":"https://content.openalex.org/works/W4404792950.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"have":[4],"garnered":[5],"significant":[6],"attention":[7],"due":[8],"to":[9,13,40,60,96,106],"their":[10,19],"remarkable":[11],"ability":[12],"process":[14],"information":[15],"across":[16],"various":[17],"languages.Despite":[18],"capabilities,":[20],"they":[21],"exhibit":[22],"inconsistencies":[23],"in":[24,28,80],"handling":[25],"identical":[26],"queries":[27],"different":[29],"languages,":[30],"presenting":[31],"challenges":[32],"for":[33,69,114],"further":[34,115],"advancement.This":[35],"paper":[36],"introduces":[37],"a":[38,55,61,63],"method":[39,93],"enhance":[41],"the":[42,101],"multilingual":[43,108],"performance":[44,77,83],"of":[45,91,104],"LLMs":[46,105],"by":[47],"aggregating":[48],"knowledge":[49,57],"from":[50],"diverse":[51],"languages.This":[52],"approach":[53],"incorporates":[54],"low-resource":[56],"detector":[58],"specific":[59],"language,":[62],"language":[64,82],"selection":[65],"process,":[66],"and":[67,72,110],"mechanisms":[68],"answer":[70],"replacement":[71],"integration.Our":[73],"experiments":[74],"demonstrate":[75],"notable":[76],"improvements,":[78],"particularly":[79],"reducing":[81],"disparity.An":[84],"ablation":[85],"study":[86],"confirms":[87],"that":[88],"each":[89],"component":[90],"our":[92],"significantly":[94],"contributes":[95],"these":[97],"enhancements.This":[98],"research":[99],"highlights":[100],"inherent":[102],"potential":[103],"harmonize":[107],"capabilities":[109],"offers":[111],"valuable":[112],"insights":[113],"exploration.Answer":[116],"(En.":[117]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-13T14:20:09.374765","created_date":"2025-10-10T00:00:00"}
