{"id":"https://openalex.org/W7204591508","doi":"https://doi.org/10.48550/arxiv.2608.26757","title":"DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?","display_name":"DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?","publication_year":2026,"publication_date":"2026-08-27","ids":{"openalex":"https://openalex.org/W7204591508","doi":"https://doi.org/10.48550/arxiv.2608.26757"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2608.26757","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.26757","pdf_url":"https://arxiv.org/pdf/2608.26757","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.26757","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5148525954","display_name":"Jiahui tang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"tang, Jiahui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075136607","display_name":"Kuicai Dong","orcid":"https://orcid.org/0000-0002-5564-0641","observed_orcids":["https://orcid.org/0000-0002-5564-0641"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Kuicai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148549133","display_name":"Dexun Li","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Dexun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113346577","display_name":"Hongchao Gu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Hongchao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148510184","display_name":"Haocheng Yu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Haocheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148476097","display_name":"Wei Han","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148502255","display_name":"Chen Zhang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5151080079","display_name":"Yong Liu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148496687","display_name":"Hao Wang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5146676370","display_name":"Enhong Chen","orcid":"https://orcid.org/0000-0002-4835-4102","observed_orcids":["https://orcid.org/0000-0002-4835-4102"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Enhong","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/T10799","display_name":"Data Visualization and Analytics","score":0.4984999895095825,"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/T10799","display_name":"Data Visualization and Analytics","score":0.4984999895095825,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.30660000443458557,"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/T10028","display_name":"Topic Modeling","score":0.14980000257492065,"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/chart-generation","display_name":"chart generation","score":0.8489999771118164},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.8140000104904175},{"id":"https://openalex.org/keywords/benchmark","display_name":"benchmark","score":0.5199999809265137},{"id":"https://openalex.org/keywords/data-extraction","display_name":"data extraction","score":0.6669999957084656},{"id":"https://openalex.org/keywords/quantitative-reasoning","display_name":"quantitative reasoning","score":0.9480000138282776},{"id":"https://openalex.org/keywords/visualization","display_name":"visualization","score":0.5870000123977661},{"id":"https://openalex.org/keywords/multimodal-data","display_name":"multimodal data","score":0.4880000054836273},{"id":"https://openalex.org/keywords/financial-filings","display_name":"financial filings","score":0.5130000114440918},{"id":"https://openalex.org/keywords/scientific-publications","display_name":"scientific publications","score":0.6549999713897705},{"id":"https://openalex.org/keywords/context-window-size","display_name":"context window size","score":0.40299999713897705}],"concepts":[{"id":"https://openalex.org/C190812933","wikidata":"https://www.wikidata.org/wiki/Q28923","display_name":"Chart","level":2,"score":0.7103000283241272},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6647999882698059},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.6204000115394592},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.5645999908447266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5063999891281128},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.42660000920295715},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4230000078678131},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.38999998569488525},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.38659998774528503},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.37059998512268066},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3116999864578247},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.28619998693466187}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.26757","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.26757","pdf_url":"https://arxiv.org/pdf/2608.26757","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.26757","doi":"https://doi.org/10.48550/arxiv.2608.26757","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.26757","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":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.26757","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.26757","pdf_url":"https://arxiv.org/pdf/2608.26757","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Faithful":[0],"chart":[1,68,81,122],"generation":[2,69,123],"in":[3,10,35,105],"real-world":[4,58],"data-science":[5],"workflows":[6],"requires":[7,125],"grounding":[8],"visualizations":[9],"scattered":[11],"evidence,":[12],"computing":[13],"chart-ready":[14],"quantities,":[15],"and":[16,38,63,74,80,101,108,129,136],"rendering":[17,82],"them":[18],"accurately.":[19],"Modern":[20],"LLMs":[21],"can":[22],"produce":[23],"visually":[24,92],"plausible,":[25],"instruction-compliant":[26],"charts,":[27],"yet":[28],"data-level":[29,97],"hallucinations":[30],"remain":[31],"difficult":[32],"to":[33],"detect":[34],"long,":[36],"noisy,":[37],"multimodal":[39,109],"contexts.":[40],"To":[41],"measure":[42],"this":[43],"gap,":[44],"we":[45],"introduce":[46],"DEEPCHART,":[47],"an":[48,71],"expert-annotated":[49],"benchmark":[50,135],"of":[51],"1,482":[52],"task-conditioned":[53],"chart-generation":[54],"instances":[55],"drawn":[56],"from":[57],"scientific":[59],"papers,":[60],"financial":[61],"filings,":[62],"ecosystem":[64],"reports.":[65],"DEEPCHART":[66],"formulates":[67],"as":[70],"Extract--Reason--Visualize":[72],"pipeline":[73],"evaluates":[75],"source-data":[76],"extraction,":[77],"derived-data":[78],"reasoning,":[79],"stage":[83],"by":[84],"stage.":[85],"Experiments":[86],"with":[87,99],"state-of-the-art":[88],"models":[89],"show":[90],"that":[91,114],"plausible":[93],"charts":[94],"often":[95],"conceal":[96],"hallucinations,":[98],"extraction":[100,128],"reasoning":[102,131],"errors":[103],"common":[104],"realistic":[106],"long":[107],"settings.":[110],"These":[111],"findings":[112],"suggest":[113],"larger":[115],"context":[116],"windows":[117],"alone":[118],"are":[119,139],"insufficient;":[120],"faithful":[121],"also":[124],"reliable":[126],"evidence":[127],"quantitative":[130],"before":[132],"rendering.":[133],"Our":[134],"associated":[137],"resources":[138],"available":[140],"at":[141],"https://github.com/tangdouer1005/DeepChart.":[142]},"counts_by_year":[],"updated_date":"2026-09-27T08:07:27.782135","created_date":"2026-08-29T00:00:00"}
