{"id":"https://openalex.org/W7124700920","doi":"https://doi.org/10.1145/3786304.3787888","title":"Can Instructed Retrieval Models Really Support Exploration?","display_name":"Can Instructed Retrieval Models Really Support Exploration?","publication_year":2026,"publication_date":"2026-02-28","ids":{"openalex":"https://openalex.org/W7124700920","doi":"https://doi.org/10.1145/3786304.3787888"},"language":null,"primary_location":{"id":"doi:10.1145/3786304.3787888","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3786304.3787888","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Human Information Interaction and Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3786304.3787888","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123346712","display_name":"Piyush Maheshwari","orcid":null},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Piyush Maheshwari","raw_affiliation_strings":["University of Massachusetts Amherst, Amherst, USA"],"raw_orcid":"https://orcid.org/0009-0004-1951-7648","affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, USA","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078949160","display_name":"Sheshera Mysore","orcid":"https://orcid.org/0000-0001-6453-3242"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]},{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sheshera Mysore","raw_affiliation_strings":["Microsoft, Seattle, USA"],"raw_orcid":"https://orcid.org/0000-0001-6453-3242","affiliations":[{"raw_affiliation_string":"Microsoft, Seattle, USA","institution_ids":["https://openalex.org/I1290206253","https://openalex.org/I58610484"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123333043","display_name":"Hamed Zamani","orcid":null},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hamed Zamani","raw_affiliation_strings":["University of Massachusetts Amherst, Amherst, USA"],"raw_orcid":"https://orcid.org/0000-0002-0800-3340","affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, USA","institution_ids":["https://openalex.org/I24603500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"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":"508","last_page":"513"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.970300018787384,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.970300018787384,"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/T10028","display_name":"Topic Modeling","score":0.004800000227987766,"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":0.004100000020116568,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.8877999782562256},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.8125},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.7098000049591064},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.4973999857902527},{"id":"https://openalex.org/keywords/exploratory-analysis","display_name":"Exploratory analysis","score":0.4602000117301941},{"id":"https://openalex.org/keywords/exploratory-research","display_name":"Exploratory research","score":0.35440000891685486}],"concepts":[{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.8877999782562256},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.8125},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7098000049591064},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.685699999332428},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.6011000275611877},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.4973999857902527},{"id":"https://openalex.org/C3018260909","wikidata":"https://www.wikidata.org/wiki/Q1322871","display_name":"Exploratory analysis","level":2,"score":0.4602000117301941},{"id":"https://openalex.org/C85973986","wikidata":"https://www.wikidata.org/wiki/Q1091731","display_name":"Exploratory research","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C2779532271","wikidata":"https://www.wikidata.org/wiki/Q445558","display_name":"Relevance feedback","level":4,"score":0.3522999882698059},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3483000099658966},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3343000113964081},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.30469998717308044},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3786304.3787888","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3786304.3787888","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Human Information Interaction and Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2601.10936","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2601.10936","pdf_url":"https://arxiv.org/pdf/2601.10936","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3786304.3787888","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3786304.3787888","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Human Information Interaction and Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5000960230827332,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Exploratory":[0],"searches":[1],"are":[2,28],"characterized":[3],"by":[4],"under-specified":[5],"goals":[6],"and":[7,24,69,120],"evolving":[8],"query":[9,22],"intents.":[10],"In":[11,38],"such":[12,35],"scenarios,":[13],"retrieval":[14,32,68],"models":[15,33],"that":[16,84,101,131],"can":[17],"capture":[18],"user-specified":[19],"nuances":[20],"in":[21],"intent":[23],"adapt":[25],"results":[26,129],"accordingly":[27],"desirable":[29],"\u2014":[30],"instruction-following":[31],"promise":[34],"a":[36],"capability.":[37],"this":[39],"work,":[40],"we":[41,98],"evaluate":[42,61],"instructed":[43,67,87,139],"retrievers":[44,88,140],"for":[45,66,73,151],"the":[46,76,85,107],"prevalent":[47],"yet":[48],"under-explored":[49],"application":[50],"of":[51,110],"aspect-conditional":[52],"seed-guided":[53],"exploration":[54],"using":[55,137,149],"an":[56],"expert-annotated":[57],"test":[58],"collection.":[59],"We":[60,82],"both":[62],"recent":[63],"LLMs":[64,71],"fine-tuned":[65],"general-purpose":[70],"prompted":[72],"ranking":[74,91,117],"with":[75,112],"highly":[77],"performant":[78],"Pairwise":[79],"Ranking":[80],"Prompting.":[81],"find":[83,100],"best":[86],"improve":[89],"on":[90],"relevance":[92,118],"compared":[93],"to":[94,106,126,158],"instruction-agnostic":[95,142],"approaches.":[96],"However,":[97],"also":[99],"instruction":[102],"following":[103],"performance,":[104],"crucial":[105],"user":[108],"experience":[109],"interacting":[111],"models,":[113,143],"does":[114],"not":[115,146],"mirror":[116],"improvements":[119],"displays":[121],"insensitivity":[122],"or":[123],"counter-intuitive":[124],"behavior":[125],"instructions.":[127,159],"Our":[128],"indicate":[130],"while":[132],"users":[133],"may":[134,145],"benefit":[135,147],"from":[136,148],"current":[138],"over":[141],"they":[144],"them":[150],"long-running":[152],"exploratory":[153],"sessions":[154],"requiring":[155],"greater":[156],"sensitivity":[157]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-01-20T00:00:00"}
