{"id":"https://openalex.org/W7128372203","doi":"https://doi.org/10.48550/arxiv.2602.06384","title":"FMBench: Adaptive Large Language Model Output Formatting","display_name":"FMBench: Adaptive Large Language Model Output Formatting","publication_year":2026,"publication_date":"2026-02-06","ids":{"openalex":"https://openalex.org/W7128372203","doi":"https://doi.org/10.48550/arxiv.2602.06384"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.06384","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125412048","display_name":"Yaoting Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yaoting","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125426774","display_name":"Yun Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125394425","display_name":"Henghui Ding","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ding, Henghui","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":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10397658,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10260","display_name":"Software Engineering Research","score":0.3156999945640564,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10260","display_name":"Software Engineering Research","score":0.3156999945640564,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11450","display_name":"Model-Driven Software Engineering Techniques","score":0.10080000013113022,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"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/T11986","display_name":"Scientific Computing and Data Management","score":0.07249999791383743,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/disk-formatting","display_name":"Disk formatting","score":0.7508000135421753},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.6001999974250793},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5992000102996826},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.5414999723434448},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.46320000290870667},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.43050000071525574},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4133000075817108},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4131999909877777}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7971000075340271},{"id":"https://openalex.org/C88006597","wikidata":"https://www.wikidata.org/wiki/Q690117","display_name":"Disk formatting","level":2,"score":0.7508000135421753},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.6001999974250793},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5992000102996826},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.5414999723434448},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.46320000290870667},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.43050000071525574},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4133000075817108},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4131999909877777},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.35370001196861267},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.32710000872612},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3138999938964844},{"id":"https://openalex.org/C67277372","wikidata":"https://www.wikidata.org/wiki/Q7449085","display_name":"Semantic role labeling","level":3,"score":0.2815999984741211},{"id":"https://openalex.org/C147494362","wikidata":"https://www.wikidata.org/wiki/Q2078905","display_name":"Troubleshooting","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C145644426","wikidata":"https://www.wikidata.org/wiki/Q169411","display_name":"Unified Modeling Language","level":3,"score":0.2581000030040741},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.25360000133514404},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.06384","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},{"id":"doi:10.48550/arxiv.2602.06384","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.06384","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":"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:doi:10.48550/arxiv.2602.06384","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Producing":[0],"outputs":[1],"that":[2,57,73,126,153,169],"satisfy":[3],"both":[4],"semantic":[5,155,173,202],"intent":[6],"and":[7,19,36,53,103,147,166,203],"format":[8],"constraints":[9],"is":[10,31,218],"essential":[11],"for":[12,68,213],"deploying":[13],"large":[14],"language":[15,99],"models":[16,75],"in":[17,33,181],"user-facing":[18],"system-integrated":[20],"workflows.":[21],"In":[22],"this":[23],"work,":[24],"we":[25,120,140],"focus":[26],"on":[27,116,144,161],"Markdown":[28,70,112,185],"formatting,":[29],"which":[30],"ubiquitous":[32],"assistants,":[34],"documentation,":[35],"tool-augmented":[37],"pipelines":[38],"but":[39],"still":[40],"prone":[41],"to":[42,106,183],"subtle,":[43],"hard-to-detect":[44],"errors":[45],"(e.g.,":[46],"broken":[47],"lists,":[48],"malformed":[49],"tables,":[50],"inconsistent":[51],"headings,":[52],"invalid":[54],"code":[55],"blocks)":[56],"can":[58],"significantly":[59],"degrade":[60],"downstream":[61],"usability.":[62],"We":[63],"present":[64],"FMBench,":[65],"a":[66,77,122,137,150,190],"benchmark":[67],"adaptive":[69],"output":[71],"formatting":[72,90],"evaluates":[74],"under":[76],"wide":[78],"range":[79],"of":[80,209],"instruction-following":[81],"scenarios":[82],"with":[83,101,131,157],"diverse":[84],"structural":[85,158,204],"requirements.":[86],"FMBench":[87],"emphasizes":[88],"real-world":[89],"behaviors":[91],"such":[92],"as":[93],"multi-level":[94],"organization,":[95],"mixed":[96],"content":[97],"(natural":[98],"interleaved":[100],"lists/tables/code),":[102],"strict":[104],"adherence":[105],"user-specified":[107],"layout":[108],"constraints.":[109],"To":[110],"improve":[111],"compliance":[113],"without":[114],"relying":[115],"hard":[117],"decoding":[118],"constraints,":[119],"propose":[121],"lightweight":[123],"alignment":[124],"pipeline":[125],"combines":[127],"supervised":[128],"fine-tuning":[129],"(SFT)":[130],"reinforcement":[132,176],"learning":[133,177],"fine-tuning.":[134],"Starting":[135],"from":[136,189],"base":[138],"model,":[139],"first":[141],"perform":[142],"SFT":[143,170,192],"instruction-response":[145],"pairs,":[146],"then":[148],"optimize":[149],"composite":[151],"objective":[152],"balances":[154],"fidelity":[156],"correctness.":[159],"Experiments":[160],"two":[162],"model":[163],"families":[164],"(OpenPangu":[165],"Qwen)":[167],"show":[168],"consistently":[171],"improves":[172],"alignment,":[174],"while":[175],"provides":[178],"additional":[179],"gains":[180],"robustness":[182],"challenging":[184],"instructions":[186],"when":[187],"initialized":[188],"strong":[191],"policy.":[193],"Our":[194],"results":[195],"also":[196],"reveal":[197],"an":[198],"inherent":[199],"trade-off":[200],"between":[201],"objectives,":[205],"highlighting":[206],"the":[207],"importance":[208],"carefully":[210],"designed":[211],"rewards":[212],"reliable":[214],"formatted":[215],"generation.":[216],"Code":[217],"available":[219],"at:":[220],"https://github.com/FudanCVL/FMBench.":[221]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-02-10T00:00:00"}
