{"id":"https://openalex.org/W7161687439","doi":"https://doi.org/10.48550/arxiv.2605.17932","title":"Prompt Compression in Diffusion Large Language Models: Evaluating LLMLingua-2 on LLaDA","display_name":"Prompt Compression in Diffusion Large Language Models: Evaluating LLMLingua-2 on LLaDA","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161687439","doi":"https://doi.org/10.48550/arxiv.2605.17932"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.17932","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17932","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.2605.17932","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023595234","display_name":"Sterling Huang","orcid":"https://orcid.org/0000-0003-1579-2760"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Sterling","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136464964","display_name":"Abigayle Brown","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brown, Abigayle","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136471049","display_name":"Jiyoo Noh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Noh, Jiyoo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129752297","display_name":"Jiakang Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jiakang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136483622","display_name":"Wantong Huo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huo, Wantong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125914740","display_name":"Kaung Myat Kyaw","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kyaw, Kaung Myat","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136494854","display_name":"Jonathan Chan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chan, Jonathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.1615999937057495,"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":0.1615999937057495,"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":0.1282999962568283,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.07029999792575836,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.7093999981880188},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.6850000023841858},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5521000027656555},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5426999926567078},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.5275999903678894},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4397999942302704},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.42260000109672546},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4178999960422516}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.7093999981880188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7003999948501587},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.6850000023841858},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5521000027656555},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5426999926567078},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5275999903678894},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4697999954223633},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4397999942302704},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.42260000109672546},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4178999960422516},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4081999957561493},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.4049000144004822},{"id":"https://openalex.org/C94835093","wikidata":"https://www.wikidata.org/wiki/Q3113333","display_name":"Data compression ratio","level":5,"score":0.4032000005245209},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4016000032424927},{"id":"https://openalex.org/C202708506","wikidata":"https://www.wikidata.org/wiki/Q7449050","display_name":"Semantic compression","level":5,"score":0.3896999955177307},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.3580000102519989},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3086000084877014},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2720000147819519},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.2696000039577484},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.2651999890804291}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.17932","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17932","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.2605.17932","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17932","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Prompt":[0],"compression":[1,25,43,59,150,167,188],"reduces":[2],"inference":[3],"cost":[4],"and":[5,48,66,78,88,182],"context":[6],"length":[7],"in":[8,102],"large":[9,30,179],"language":[10,31,180],"models,":[11],"but":[12],"prior":[13],"evaluations":[14],"focus":[15],"primarily":[16,153],"on":[17,45],"autoregressive":[18,171],"architectures.":[19],"This":[20],"study":[21],"investigates":[22],"whether":[23],"prompt":[24,64,166],"transfers":[26],"effectively":[27],"to":[28,177],"diffusion":[29,103,178],"models":[32,172,181],"(DLLMs)":[33],"using":[34,50,83],"LLMLingua-2,":[35],"specifically":[36],"the":[37,184],"8B-parameter":[38],"DLLM":[39],"LLaDA.":[40],"We":[41],"evaluate":[42],"performance":[44],"GSM8K,":[46],"DUC2004,":[47],"ShareGPT":[49],"250":[51],"prompts":[52,129],"per":[53],"dataset":[54],"at":[55],"an":[56],"approximate":[57],"2$\\times$":[58],"ratio,":[60],"across":[61],"mathematical":[62,113],"reasoning,":[63],"reconstruction,":[65],"summarization":[67],"tasks.":[68],"Outputs":[69],"generated":[70],"from":[71],"original":[72],"prompts,":[73,75,77],"compressed":[74],"reconstructed":[76],"reconstructed-prompt":[79],"reasoning":[80,114],"were":[81],"compared":[82],"exact-match":[84],"accuracy,":[85],"BLEU,":[86],"ROUGE,":[87],"BERTScore.":[89],"Results":[90],"show":[91],"that":[92,126,149,165],"semantic":[93,119,160],"preservation":[94],"does":[95],"not":[96,174],"necessarily":[97],"imply":[98],"stable":[99,137],"downstream":[100],"behavior":[101],"models.":[104],"Summarization":[105],"tasks":[106],"remained":[107],"comparatively":[108],"robust":[109],"under":[110],"compression,":[111],"while":[112],"degraded":[115],"substantially":[116],"despite":[117],"high":[118],"similarity":[120],"scores.":[121],"Reconstruction":[122],"experiments":[123],"further":[124],"showed":[125],"semantically":[127],"similar":[128],"may":[130],"still":[131],"omit":[132],"reasoning-critical":[133],"information":[134,156],"required":[135],"for":[136,170],"denoising.":[138],"Across":[139],"tasks,":[140],"BERTScore":[141],"recall":[142],"was":[143],"consistently":[144],"lower":[145],"than":[146,159],"precision,":[147],"suggesting":[148],"failures":[151],"are":[152],"driven":[154],"by":[155],"omission":[157],"rather":[158],"drift.":[161],"These":[162],"findings":[163],"indicate":[164],"methods":[168],"designed":[169],"do":[173],"transfer":[175],"uniformly":[176],"motivate":[183],"development":[185],"of":[186],"diffusion-aware":[187],"strategies.":[189]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-20T00:00:00"}