{"id":"https://openalex.org/W7165060653","doi":"https://doi.org/10.48550/arxiv.2606.17609","title":"The Benchmark Illusion: Pruned LLMs Can Pass Multiple Choice but Fail to Answer","display_name":"The Benchmark Illusion: Pruned LLMs Can Pass Multiple Choice but Fail to Answer","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W7165060653","doi":"https://doi.org/10.48550/arxiv.2606.17609"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.17609","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17609","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.17609","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138819462","display_name":"Rui Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121042310","display_name":"L G Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Lu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138809171","display_name":"Jiayang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jiayang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100524221","display_name":"Zesheng Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Zesheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138791666","display_name":"Tianshuo Cong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cong, Tianshuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138777570","display_name":"Zheng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zheng","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/T10028","display_name":"Topic Modeling","score":0.7192999720573425,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.7192999720573425,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.049800001084804535,"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/T13629","display_name":"Text Readability and Simplification","score":0.037300001829862595,"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/benchmark","display_name":"Benchmark (surveying)","score":0.7878999710083008},{"id":"https://openalex.org/keywords/ask-price","display_name":"Ask price","score":0.7615000009536743},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6966999769210815},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6136999726295471},{"id":"https://openalex.org/keywords/usability","display_name":"Usability","score":0.399399995803833},{"id":"https://openalex.org/keywords/greedy-algorithm","display_name":"Greedy algorithm","score":0.36399999260902405},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.3637000024318695}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7878999710083008},{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.7615000009536743},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7488999962806702},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6966999769210815},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6136999726295471},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5163999795913696},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47350001335144043},{"id":"https://openalex.org/C170130773","wikidata":"https://www.wikidata.org/wiki/Q216378","display_name":"Usability","level":2,"score":0.399399995803833},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.36399999260902405},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.3637000024318695},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.31929999589920044},{"id":"https://openalex.org/C176730311","wikidata":"https://www.wikidata.org/wiki/Q867668","display_name":"Multiple choice","level":3,"score":0.3041999936103821},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C128099668","wikidata":"https://www.wikidata.org/wiki/Q573952","display_name":"Lazy evaluation","level":3,"score":0.2711000144481659},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.25600001215934753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.17609","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17609","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":"doi:10.48550/arxiv.2606.17609","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17609","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":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5722188949584961,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Compressing":[0],"large":[1],"language":[2],"models":[3,90,145],"reduces":[4],"memory":[5],"use":[6],"and":[7,77],"inference":[8],"cost,":[9],"but":[10,116],"it":[11,46,53,118],"can":[12,132,152,159],"also":[13],"create":[14],"failures":[15],"that":[16],"standard":[17],"benchmarks":[18,131],"miss.":[19],"A":[20],"pruned":[21],"model":[22],"may":[23],"still":[24,98],"perform":[25],"well":[26],"on":[27,149,156],"multiple-choice":[28,104,130],"evaluations,":[29],"yet":[30],"fail":[31,92],"to":[32,58],"answer":[33,56,102,111],"the":[34,48,55,61,73,100,110,134],"same":[35,74],"question":[36,67,70],"in":[37,93],"open":[38,95],"generation.":[39],"We":[40,64,80],"ask":[41],"what":[42,150,157],"pruning":[43],"changes:":[44],"does":[45,52],"erase":[47],"correct":[49,101],"answer,":[50],"or":[51,125],"make":[54],"harder":[57],"produce":[59],"as":[60],"top":[62],"output?":[63],"study":[65],"this":[66],"with":[68,121],"multilingual":[69],"answering,":[71],"tracking":[72],"questions":[75],"before":[76],"after":[78],"pruning.":[79],"find":[81],"a":[82],"benchmark":[83],"illusion.":[84],"Under":[85],"high-sparsity":[86],"pruning,":[87],"especially":[88],"Wanda,":[89],"often":[91,119],"greedy":[94],"generation":[96],"while":[97],"selecting":[99],"under":[103],"scoring.":[105],"In":[106],"these":[107],"recognition-only":[108],"errors,":[109],"is":[112],"usually":[113],"not":[114,154],"gone,":[115],"demoted:":[117],"reappears":[120],"beam":[122],"search,":[123],"sampling,":[124],"one":[126],"in-context":[127],"example.":[128],"Overall,":[129],"overstate":[133],"usability":[135],"of":[136],"compressed":[137],"LLMs,":[138],"creating":[139],"an":[140],"evaluation":[141],"blind":[142],"spot.":[143],"Compressed":[144],"should":[146],"be":[147],"tested":[148],"they":[151,158],"produce,":[153],"only":[155],"recognize.":[160]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-18T00:00:00"}