{"id":"https://openalex.org/W7166864620","doi":"https://doi.org/10.18653/v1/2026.findings-acl.887","title":"Can AI Revise Research Papers with Human Review Feedback? An Empirical Study and Benchmark","display_name":"Can AI Revise Research Papers with Human Review Feedback? An Empirical Study and Benchmark","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166864620","doi":"https://doi.org/10.18653/v1/2026.findings-acl.887"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.887","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.887","pdf_url":"https://aclanthology.org/2026.findings-acl.887.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":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-acl.887.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139799141","display_name":"Zihan Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zihan Luo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139750529","display_name":"Hong Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hong Huang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139732141","display_name":"Jianxun Lian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianxun Lian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139715469","display_name":"Yu Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu Chang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139740186","display_name":"Xing Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xing Xie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139777859","display_name":"Hai Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hai Jin","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":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.85357625,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"17876","last_page":"17893"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.12139999866485596,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.12139999866485596,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10206","display_name":"Meta-analysis and systematic reviews","score":0.08910000324249268,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.039900001138448715,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"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/empirical-research","display_name":"Empirical research","score":0.6812000274658203},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5457000136375427},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.2522999942302704}],"concepts":[{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.6812000274658203},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5748999714851379},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5457000136375427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4300999939441681},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.41850000619888306},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.3953000009059906},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3407000005245209},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.32919999957084656},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2996000051498413},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2689000070095062},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.887","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.887","pdf_url":"https://aclanthology.org/2026.findings-acl.887.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":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-acl.887","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.887","pdf_url":"https://aclanthology.org/2026.findings-acl.887.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":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8899279166","display_name":null,"funder_award_id":"62127808","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321883","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166864620.pdf","grobid_xml":"https://content.openalex.org/works/W7166864620.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"rise":[1],"of":[2,27,77,135],"Human-AI":[3],"collaboration":[4,29],"can":[5,65],"effectively":[6],"speed":[7],"up":[8],"the":[9,31,42,75],"research":[10],"process":[11],"for":[12],"experts":[13],"and":[14,44,87,142,150],"allow":[15],"anyone":[16],"with":[17,38],"critical":[18],"thinking":[19],"skills":[20,76],"to":[21,34,46],"conduct":[22],"innovative":[23],"work.A":[24],"key":[25],"part":[26],"this":[28,51,129],"is":[30],"AI's":[32],"ability":[33],"improve":[35],"a":[36,99,106,132],"paper":[37,83,88],"human":[39,96,140],"feedback-updating":[40],"both":[41],"text":[43],"experiments":[45],"meet":[47],"high":[48],"standards.To":[49],"evaluate":[50],"skill,":[52],"we":[53,102],"introduce":[54],"ReviseBench,":[55],"an":[56],"extensible":[57],"benchmark":[58],"built":[59],"on":[60,82,118],"real":[61],"academic":[62],"data":[63,72,152],"that":[64,121],"be":[66],"easily":[67],"scaled":[68],"via":[69],"agent-driven":[70],"automated":[71],"collection.It":[73],"tests":[74],"Large":[78],"Language":[79],"Models":[80],"(LLMs)":[81],"interpretation,":[84],"experimental":[85],"implementation,":[86],"formulation,":[89],"using":[90],"authors'":[91],"camera-ready":[92],"versions":[93],"as":[94],"natural":[95],"baselines.To":[97],"facilitate":[98],"fine-grained":[100],"assessment,":[101],"further":[103],"propose":[104],"ReviseArena,":[105],"platform":[107],"supporting":[108],"pair-wise":[109],"comparisons":[110],"between":[111],"different":[112],"AIrevised":[113],"papers.Our":[114],"initial":[115],"evaluation":[116],"results":[117],"ReviseBench":[119],"reveal":[120],"even":[122],"state-of-theart":[123],"foundation":[124],"LLMs":[125],"struggle":[126],"significantly":[127],"in":[128],"domain,":[130],"achieving":[131],"win":[133],"rate":[134],"less":[136],"than":[137],"10%":[138],"against":[139],"experts,":[141],"facing":[143],"issues":[144],"like":[145],"incremental":[146],"revision,":[147,149],"unprofessional":[148],"potential":[151],"fabrication.":[153]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}
