{"id":"https://openalex.org/W7160325330","doi":"https://doi.org/10.48550/arxiv.2605.01846","title":"Do Large Language Models Plan Answer Positions? Position Bias in Multiple-Choice Question Generation","display_name":"Do Large Language Models Plan Answer Positions? Position Bias in Multiple-Choice Question Generation","publication_year":2026,"publication_date":"2026-05-03","ids":{"openalex":"https://openalex.org/W7160325330","doi":"https://doi.org/10.48550/arxiv.2605.01846"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.01846","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01846","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.2605.01846","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135378624","display_name":"Xuemei Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Xuemei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135311873","display_name":"Xufeng Duan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duan, Xufeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135319474","display_name":"Zhenguang G. Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Zhenguang G.","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":0.8575999736785889,"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":0.8575999736785889,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.048500001430511475,"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/T12031","display_name":"Speech and dialogue systems","score":0.009399999864399433,"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/position","display_name":"Position (finance)","score":0.7339000105857849},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.5873000025749207},{"id":"https://openalex.org/keywords/plan","display_name":"Plan (archaeology)","score":0.5598000288009644},{"id":"https://openalex.org/keywords/position-paper","display_name":"Position paper","score":0.4702000021934509},{"id":"https://openalex.org/keywords/text-generation","display_name":"Text generation","score":0.36489999294281006},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.36070001125335693}],"concepts":[{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.7339000105857849},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.5873000025749207},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.5598000288009644},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5385000109672546},{"id":"https://openalex.org/C78780964","wikidata":"https://www.wikidata.org/wiki/Q7233193","display_name":"Position paper","level":2,"score":0.4702000021934509},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4327000081539154},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3880000114440918},{"id":"https://openalex.org/C2985684807","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Text generation","level":2,"score":0.36489999294281006},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.36070001125335693},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3481000065803528},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.335099995136261},{"id":"https://openalex.org/C2989277270","wikidata":"https://www.wikidata.org/wiki/Q168338","display_name":"Behavioral analysis","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2540999948978424},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.2531999945640564},{"id":"https://openalex.org/C48243021","wikidata":"https://www.wikidata.org/wiki/Q932522","display_name":"Strategic planning","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.01846","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01846","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.2605.01846","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01846","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":[],"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":[2,42],"(LLMs)":[3],"are":[4,54],"increasingly":[5],"used":[6],"to":[7,107],"generate":[8],"multiple-choice":[9],"questions":[10],"(MCQs),":[11],"where":[12],"correct":[13,86],"answers":[14],"should":[15],"ideally":[16],"be":[17,94],"uniformly":[18],"distributed":[19],"across":[20],"options.":[21],"However,":[22],"we":[23,49,68,103],"observe":[24],"that":[25,51,74,90,118],"LLMs":[26,38,143],"exhibit":[27],"systematic":[28],"position":[29,92,129],"biases":[30,53],"during":[31,97],"generation.":[32,98],"Through":[33],"extensive":[34],"experiments":[35,71],"with":[36,56],"10":[37],"and":[39,72,111,125,144,155],"5":[40],"vision-language":[41],"(VLMs)":[43],"on":[44,100],"three":[45],"MCQ":[46,153],"generation":[47,150],"tasks,":[48],"show":[50,117],"these":[52],"structured,":[55],"similar":[57],"patterns":[58],"emerging":[59],"within":[60],"model":[61],"families.":[62],"To":[63],"investigate":[64],"the":[65,78,85,146],"underlying":[66],"mechanisms,":[67],"conduct":[69],"probing":[70],"find":[73],"hidden":[75],"representations":[76,110],"in":[77,142],"question":[79],"stem":[80],"encode":[81],"predictive":[82],"signals":[83],"of":[84,148],"answer":[87,91,113,128],"position,":[88],"suggesting":[89],"may":[93],"implicitly":[95],"planned":[96],"Building":[99],"this":[101],"insight,":[102],"apply":[104],"activation":[105],"steering":[106,119],"manipulate":[108],"internal":[109],"influence":[112],"position.":[114],"Our":[115,131],"results":[116],"can":[120],"partially":[121],"control":[122],"positional":[123,140],"preferences":[124],"substantially":[126],"shift":[127],"distributions.":[130],"findings":[132],"provide":[133],"a":[134],"practical":[135],"framework":[136],"for":[137,151],"studying":[138],"implicit":[139],"planning":[141],"highlight":[145],"importance":[147],"controllable":[149],"reliable":[152],"construction":[154],"evaluation.":[156]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-06T00:00:00"}
