{"id":"https://openalex.org/W7134824532","doi":"https://doi.org/10.48550/arxiv.2603.08148","title":"Gradually Excavating External Knowledge for Implicit Complex Question Answering","display_name":"Gradually Excavating External Knowledge for Implicit Complex Question Answering","publication_year":2026,"publication_date":"2026-03-09","ids":{"openalex":"https://openalex.org/W7134824532","doi":"https://doi.org/10.48550/arxiv.2603.08148"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.08148","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.08148","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.08148","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128630435","display_name":"Chang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Chang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128642381","display_name":"Xiaoguang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiaoguang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128686608","display_name":"Lifeng Shang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shang, Lifeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128654679","display_name":"Xin Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128632692","display_name":"Qun Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Qun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128684583","display_name":"Edmund Y. Lam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lam, Edmund Y.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128671510","display_name":"Ngai Wong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wong, Ngai","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/T10028","display_name":"Topic Modeling","score":0.6837999820709229,"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":0.6837999820709229,"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":0.147599995136261,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.03269999846816063,"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/leverage","display_name":"Leverage (statistics)","score":0.7470999956130981},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.6139000058174133},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5906000137329102},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.4205999970436096},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4097000062465668},{"id":"https://openalex.org/keywords/implicit-knowledge","display_name":"Implicit knowledge","score":0.37119999527931213}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7470999956130981},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.6139000058174133},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5960999727249146},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5906000137329102},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.46810001134872437},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.4205999970436096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41370001435279846},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4097000062465668},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.3887999951839447},{"id":"https://openalex.org/C2986065213","wikidata":"https://www.wikidata.org/wiki/Q743861","display_name":"Implicit knowledge","level":2,"score":0.37119999527931213},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.36329999566078186},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.3580000102519989},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2791999876499176},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.2614000141620636},{"id":"https://openalex.org/C134752490","wikidata":"https://www.wikidata.org/wiki/Q374182","display_name":"Logical consequence","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C2986750623","wikidata":"https://www.wikidata.org/wiki/Q830170","display_name":"Knowledge creation","level":3,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.08148","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.08148","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.08148","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.08148","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Recently,":[0],"large":[1],"language":[2],"models":[3],"(LLMs)":[4],"have":[5],"gained":[6],"much":[7],"attention":[8],"for":[9,19,60,128,153],"the":[10,28,33,86,89,126,134],"emergence":[11],"of":[12,85,147],"human-comparable":[13],"capabilities":[14],"and":[15,45,68,73,123],"huge":[16],"potential.":[17],"However,":[18],"open-domain":[20,61],"implicit":[21],"question-answering":[22],"problems,":[23],"LLMs":[24,66],"may":[25],"not":[26],"be":[27],"ultimate":[29],"solution":[30],"due":[31],"to":[32,94,108],"reasons":[34],"of:":[35],"1)":[36],"uncovered":[37],"or":[38,101],"out-of-date":[39],"domain":[40],"knowledge,":[41],"2)":[42],"one-shot":[43],"generation":[44],"hence":[46],"restricted":[47],"comprehensiveness.":[48],"To":[49],"this":[50,52],"end,":[51],"work":[53],"proposes":[54],"a":[55,103,112],"gradual":[56],"knowledge":[57,100,122],"excavation":[58],"framework":[59],"complex":[62,130],"question":[63],"answering,":[64],"where":[65],"iteratively":[67],"actively":[69],"acquire":[70],"external":[71,99,121],"information,":[72],"then":[74],"reason":[75],"based":[76],"on":[77,133],"acquired":[78],"historical":[79],"knowledge.":[80],"Specifically,":[81],"during":[82],"each":[83],"step":[84],"solving":[87,129],"process,":[88],"model":[90],"selects":[91],"an":[92],"action":[93],"execute,":[95],"such":[96],"as":[97],"querying":[98],"performing":[102],"single":[104],"logical":[105],"reasoning":[106],"step,":[107],"gradually":[109],"progress":[110],"toward":[111],"final":[113],"answer.":[114],"Our":[115],"method":[116,138],"can":[117],"effectively":[118],"leverage":[119],"plug-and-play":[120],"dynamically":[124],"adjust":[125],"strategy":[127],"questions.":[131],"Evaluated":[132],"StrategyQA":[135],"dataset,":[136],"our":[137],"achieves":[139],"78.17%":[140],"accuracy":[141],"with":[142],"less":[143],"than":[144],"6%":[145],"parameters":[146],"its":[148],"competitors,":[149],"setting":[150],"new":[151],"SOTA":[152],"~10B-scale":[154],"LLMs.":[155]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-11T00:00:00"}