{"id":"https://openalex.org/W7160273190","doi":"https://doi.org/10.48550/arxiv.2605.01357","title":"On Stable Long-Form Generation: Benchmarking and Mitigating Length Volatility","display_name":"On Stable Long-Form Generation: Benchmarking and Mitigating Length Volatility","publication_year":2026,"publication_date":"2026-05-02","ids":{"openalex":"https://openalex.org/W7160273190","doi":"https://doi.org/10.48550/arxiv.2605.01357"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.01357","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01357","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.01357","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100297688","display_name":"Zhitao He","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"He, Zhitao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135400185","display_name":"Haolin Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Haolin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135303751","display_name":"Rui Min","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Min, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135301167","display_name":"Zeyu Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Zeyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124957280","display_name":"Yi R. Fung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fung, Yi R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100297688"],"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.25519999861717224,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.25519999861717224,"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":0.2020999938249588,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.06539999693632126,"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/benchmarking","display_name":"Benchmarking","score":0.8066999912261963},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.75},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5246999859809875},{"id":"https://openalex.org/keywords/implied-volatility","display_name":"Implied volatility","score":0.4438999891281128},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.43880000710487366},{"id":"https://openalex.org/keywords/volatility-smile","display_name":"Volatility smile","score":0.3630000054836273}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.8066999912261963},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.75},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.576200008392334},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5246999859809875},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5217999815940857},{"id":"https://openalex.org/C24189920","wikidata":"https://www.wikidata.org/wiki/Q1660345","display_name":"Implied volatility","level":3,"score":0.4438999891281128},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.43880000710487366},{"id":"https://openalex.org/C13290067","wikidata":"https://www.wikidata.org/wiki/Q915788","display_name":"Volatility smile","level":3,"score":0.3630000054836273},{"id":"https://openalex.org/C117996083","wikidata":"https://www.wikidata.org/wiki/Q5473296","display_name":"Forward volatility","level":4,"score":0.32670000195503235},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.30660000443458557},{"id":"https://openalex.org/C60092789","wikidata":"https://www.wikidata.org/wiki/Q7301291","display_name":"Realized variance","level":3,"score":0.28060001134872437},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.26980000734329224},{"id":"https://openalex.org/C85393063","wikidata":"https://www.wikidata.org/wiki/Q596307","display_name":"Stochastic volatility","level":3,"score":0.26739999651908875},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2603999972343445}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.01357","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01357","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.01357","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01357","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":false,"raw_source_name":null,"raw_type":"article"},"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":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"excel":[4],"at":[5],"long-context":[6],"understanding":[7],"but":[8,38],"exhibit":[9],"significant":[10,35],"limitations":[11],"in":[12,53,65,153],"long-form":[13,82,107,134,150],"generation.":[14,83],"Existing":[15],"studies":[16],"primarily":[17],"focus":[18],"on":[19,141],"single-generation":[20],"quality,":[21],"generally":[22],"overlooking":[23],"the":[24,27,42,63,78,128,144,164,169,176],"volatility":[25,30,80,178],"of":[26,81,133,148,168],"output.":[28],"This":[29],"not":[31],"only":[32],"leads":[33],"to":[34,75,94,105,124],"computational":[36],"costs":[37],"also":[39],"severely":[40],"impacts":[41],"models'":[43],"reliable":[44],"application.":[45],"To":[46],"address":[47],"this":[48,102],"gap,":[49],"our":[50,159],"work":[51],"unfolds":[52],"three":[54],"stages:":[55],"benchmarking,":[56],"probing,":[57],"and":[58,131,156,174],"mitigation.":[59],"We":[60],"first":[61,145],"propose":[62,111],"VOlatility":[64],"Long-form":[66],"Text":[67],"Benchmark":[68],"(VOLTBench),":[69],"a":[70,118],"novel":[71],"heterogeneous-task":[72],"benchmark":[73],"designed":[74,123],"systematically":[76],"quantify":[77],"length":[79,129,167,177],"Subsequently,":[84],"by":[85,172,179],"analyzing":[86],"attention":[87],"traces,":[88],"we":[89,110],"conduct":[90],"an":[91],"in-depth":[92],"probe":[93],"identify":[95],"several":[96],"common":[97],"internal":[98],"patterns":[99],"that":[100,158],"cause":[101],"volatility.":[103],"Finally,":[104],"mitigate":[106],"output":[108,151,166],"volatility,":[109],"Stable":[112],"Generation":[113],"via":[114],"Logits":[115],"Boosting":[116],"(GLoBo),":[117],"lightweight":[119],"decoding-stage":[120],"optimization":[121],"strategy,":[122],"significantly":[125],"enhance":[126],"both":[127],"accuracy":[130],"stability":[132],"generation":[135,184],"without":[136],"additional":[137],"training.":[138],"Extensive":[139],"experiments":[140],"VOLTBench":[142],"provide":[143],"systematic":[146],"confirmation":[147],"severe":[149],"instability":[152],"mainstream":[154],"models":[155],"validate":[157],"proposed":[160],"method":[161],"successfully":[162],"improves":[163],"mean":[165],"base":[170],"model":[171],"148%":[173],"reduces":[175],"69%,":[180],"while":[181],"maintaining":[182],"high":[183],"quality.":[185]},"counts_by_year":[],"updated_date":"2026-05-06T06:10:43.113611","created_date":"2026-05-06T00:00:00"}