{"id":"https://openalex.org/W7153728228","doi":"https://doi.org/10.1145/3772318.3790721","title":"Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Framework","display_name":"Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Framework","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7153728228","doi":"https://doi.org/10.1145/3772318.3790721"},"language":null,"primary_location":{"id":"doi:10.1145/3772318.3790721","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3772318.3790721","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3772318.3790721","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yuchen He","orcid":"https://orcid.org/0009-0003-1035-4347"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuchen He","raw_affiliation_strings":["Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0009-0003-1035-4347","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133446682","display_name":"Peizhi Ying","orcid":"https://orcid.org/0009-0009-2494-8131"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peizhi Ying","raw_affiliation_strings":["Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0009-0009-2494-8131","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052752621","display_name":"Liqi Cheng","orcid":"https://orcid.org/0009-0000-8868-5101"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liqi Cheng","raw_affiliation_strings":["Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0009-0000-8868-5101","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133451444","display_name":"Kuilin Peng","orcid":"https://orcid.org/0009-0003-0945-3989"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kuilin Peng","raw_affiliation_strings":["Guangdong University of Technology, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0009-0003-0945-3989","affiliations":[{"raw_affiliation_string":"Guangdong University of Technology, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yuan Tian","orcid":"https://orcid.org/0009-0005-6089-7694"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Tian","raw_affiliation_strings":["Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0009-0005-6089-7694","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049050148","display_name":"Dazhen Deng","orcid":"https://orcid.org/0000-0002-9057-8353"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dazhen Deng","raw_affiliation_strings":["Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0002-9057-8353","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073986937","display_name":"Yingcai Wu","orcid":"https://orcid.org/0000-0002-1119-3237"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingcai Wu","raw_affiliation_strings":["Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0002-1119-3237","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.2694000005722046,"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"}},"topics":[{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.2694000005722046,"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":0.1623000055551529,"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/T13523","display_name":"Mathematics, Computing, and Information Processing","score":0.11299999803304672,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/training","display_name":"Training (meteorology)","score":0.6485000252723694},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5659000277519226},{"id":"https://openalex.org/keywords/chart","display_name":"Chart","score":0.4763000011444092},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.37209999561309814},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3605000078678131}],"concepts":[{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6485000252723694},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6082000136375427},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5659000277519226},{"id":"https://openalex.org/C190812933","wikidata":"https://www.wikidata.org/wiki/Q28923","display_name":"Chart","level":2,"score":0.4763000011444092},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41100001335144043},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.37209999561309814},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3605000078678131},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3407000005245209},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3400000035762787},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3264000117778778}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3772318.3790721","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3772318.3790721","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2606.29808","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2606.29808","pdf_url":"https://arxiv.org/pdf/2606.29808","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3772318.3790721","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3772318.3790721","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3196381497","display_name":"\u9762\u5411\u901a\u7528\u5927\u6a21\u578b\u7684\u5b89\u5168\u5bf9\u9f50\u53ef\u89c6\u5206\u6790\u65b9\u6cd5","funder_award_id":"LD25F020003","funder_id":"https://openalex.org/F4320338464","funder_display_name":"Natural Science Foundation of Zhejiang Province"},{"id":"https://openalex.org/G3405827317","display_name":null,"funder_award_id":"62421003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6133567474","display_name":null,"funder_award_id":"62402428","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/F4320322927","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884"},{"id":"https://openalex.org/F4320338464","display_name":"Natural Science Foundation of Zhejiang Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W2032191478","https://openalex.org/W2053604034","https://openalex.org/W2059216172","https://openalex.org/W2099482242","https://openalex.org/W2401208927","https://openalex.org/W2516678343","https://openalex.org/W2595457065","https://openalex.org/W2886887279","https://openalex.org/W2887781372","https://openalex.org/W2941366772","https://openalex.org/W2963420691","https://openalex.org/W2977058691","https://openalex.org/W3005666391","https://openalex.org/W3009051979","https://openalex.org/W3009518609","https://openalex.org/W3021218348","https://openalex.org/W3116465435","https://openalex.org/W3118703676","https://openalex.org/W4220657226","https://openalex.org/W4280596375","https://openalex.org/W4285255856","https://openalex.org/W4307959641","https://openalex.org/W4366549398","https://openalex.org/W4379374334","https://openalex.org/W4385570745","https://openalex.org/W4385570934","https://openalex.org/W4389523846","https://openalex.org/W4391094120","https://openalex.org/W4392026410","https://openalex.org/W4392172801","https://openalex.org/W4396851234","https://openalex.org/W4402401986","https://openalex.org/W4402402086","https://openalex.org/W4402669758","https://openalex.org/W4402670747","https://openalex.org/W4402683795","https://openalex.org/W4402684237","https://openalex.org/W4402727764","https://openalex.org/W4403081466","https://openalex.org/W4403792079","https://openalex.org/W4404356490","https://openalex.org/W4404781890","https://openalex.org/W4404782963","https://openalex.org/W4405179243","https://openalex.org/W4409748645","https://openalex.org/W4409767214","https://openalex.org/W4412887790","https://openalex.org/W4412945598","https://openalex.org/W4413848512","https://openalex.org/W4414564600","https://openalex.org/W4415795850","https://openalex.org/W4416036221","https://openalex.org/W4416429374","https://openalex.org/W4416429423","https://openalex.org/W7133193597"],"related_works":[],"abstract_inverted_index":{"Chart":[0],"data":[1,5,52,69,97,148],"extraction,":[2],"which":[3],"reverse-engineers":[4],"tables":[6],"from":[7,99],"chart":[8,44,96,147],"images,":[9],"is":[10],"essential":[11],"for":[12,43,145],"reproducibility,":[13],"analysis,":[14],"retrieval,":[15],"and":[16,25,103],"redesign.":[17],"Existing":[18],"interactive":[19],"tools":[20],"are":[21],"reliable":[22,146],"but":[23],"tedious,":[24],"mixed-initiative":[26,143],"systems,":[27],"while":[28,78],"more":[29],"efficient,":[30],"lack":[31],"generalizability.":[32],"Recent":[33],"multimodal":[34],"large":[35],"language":[36],"models":[37],"(MLLMs)":[38],"offer":[39],"a":[40,62,100,109,130],"unified":[41],"interface":[42],"interpretation,":[45],"yet":[46],"their":[47],"ability":[48],"to":[49,71,114],"extract":[50],"accurate":[51],"tables,":[53],"especially":[54],"without":[55,68],"visible":[56],"labels,":[57],"remains":[58],"unclear.":[59],"We":[60],"build":[61],"benchmark":[63],"featuring":[64],"diverse":[65],"real-world":[66],"charts":[67],"labels":[70],"evaluate":[72],"this":[73],"capability.":[74],"Results":[75],"show":[76],"that,":[77],"current":[79],"MLLMs":[80],"reliably":[81],"reconstruct":[82],"table":[83],"structures,":[84],"they":[85],"struggle":[86],"with":[87,129],"precise":[88],"value":[89],"recovery.":[90],"To":[91],"address":[92],"this,":[93],"we":[94],"revisit":[95],"extraction":[98,106],"human-centered":[101],"perspective":[102],"argue":[104],"that":[105,138],"should":[107],"follow":[108],"progressive":[110],"learning":[111],"process":[112],"similar":[113],"how":[115],"people":[116],"read":[117],"charts.":[118],"Our":[119],"training":[120],"framework":[121],"substantially":[122],"improves":[123],"numerical":[124],"accuracy,":[125],"achieving":[126],"state-of-the-art":[127],"performance":[128],"7B-parameter":[131],"model.":[132],"A":[133],"user":[134],"study":[135],"further":[136],"shows":[137],"our":[139],"model":[140],"effectively":[141],"supports":[142],"workflows":[144],"extraction.":[149]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-04-13T00:00:00"}
