{"id":"https://openalex.org/W7162462546","doi":"https://doi.org/10.48550/arxiv.2605.25204","title":"Clarification Is Not Enough: Post-Clarification Answering Remains the Bottleneck in Multi-Turn QA","display_name":"Clarification Is Not Enough: Post-Clarification Answering Remains the Bottleneck in Multi-Turn QA","publication_year":2026,"publication_date":"2026-05-24","ids":{"openalex":"https://openalex.org/W7162462546","doi":"https://doi.org/10.48550/arxiv.2605.25204"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.25204","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25204","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.2605.25204","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074353443","display_name":"Jinyan Su","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Jinyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102269767","display_name":"Jennifer Healey","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Healey, Jennifer","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/T13274","display_name":"Expert finding and Q&A systems","score":0.5188000202178955,"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/T13274","display_name":"Expert finding and Q&A systems","score":0.5188000202178955,"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":0.3772999942302704,"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/T12031","display_name":"Speech and dialogue systems","score":0.02070000022649765,"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/ask-price","display_name":"Ask price","score":0.8184999823570251},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.6507999897003174},{"id":"https://openalex.org/keywords/preference-elicitation","display_name":"Preference elicitation","score":0.5935999751091003},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.5899999737739563},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5879999995231628}],"concepts":[{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.8184999823570251},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6899999976158142},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.6507999897003174},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.5935999751091003},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.5899999737739563},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5879999995231628},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45249998569488525},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3310000002384186},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2928999960422516},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.266400009393692},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C3019144022","wikidata":"https://www.wikidata.org/wiki/Q4124998","display_name":"Questions and answers","level":2,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.25204","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25204","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.2605.25204","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25204","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":[{"id":"https://metadata.un.org/sdg/10","score":0.40400102734565735,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Pluralistic":[0],"alignment":[1,23],"requires":[2],"systems":[3],"to":[4,6,61],"adapt":[5],"diverse":[7],"user":[8],"values,":[9],"communication":[10],"styles,":[11],"and":[12,69,118],"contextual":[13],"assumptions.":[14],"We":[15,37,84],"believe":[16],"that":[17,90,116],"a":[18,55,63],"foundational":[19],"prerequisite":[20],"for":[21],"such":[22],"enabling":[24],"accurate":[25],"preference":[26,42],"elicitation":[27,43],"from":[28],"people":[29],"when":[30,106],"their":[31],"intent":[32],"is":[33,82,124],"under-specified":[34],"or":[35,66],"ambiguous.":[36],"study":[38],"the":[39,50,74,79,87,95,107,110,121,125],"problem":[40,51],"of":[41],"in":[44,128],"multi-turn":[45,129],"question":[46,65],"answering":[47],"by":[48],"decomposing":[49],"into":[52],"two":[53],"components:":[54],"\\textbf{clarification":[56],"policy},":[57],"which":[58,72],"decides":[59],"whether":[60],"ask":[62],"clarifying":[64],"answer":[67,77,100],"directly,":[68],"\\textbf{post-clarification":[70],"answering},":[71],"produces":[73],"correct":[75,111],"final":[76,99],"once":[78],"missing":[80],"information":[81],"provided.":[83],"show,":[85],"using":[86],"PACIFIC":[88],"benchmark,":[89],"supervised":[91],"fine-tuning":[92],"rapidly":[93],"improves":[94],"clarification":[96],"policy,":[97],"however,":[98],"accuracy":[101],"remains":[102],"substantially":[103],"lower":[104],"even":[105],"model":[108],"takes":[109],"action.":[112],"This":[113],"gap":[114,127],"indicates":[115],"understanding":[117],"correctly":[119],"interpreting":[120],"user's":[122],"response":[123],"critical":[126],"question-answering":[130],"systems.":[131]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}
