{"id":"https://openalex.org/W7127328393","doi":"https://doi.org/10.48550/arxiv.2602.00665","title":"Can Small Language Models Handle Context-Summarized Multi-Turn Customer-Service QA? A Synthetic Data-Driven Comparative Evaluation","display_name":"Can Small Language Models Handle Context-Summarized Multi-Turn Customer-Service QA? A Synthetic Data-Driven Comparative Evaluation","publication_year":2026,"publication_date":"2026-01-31","ids":{"openalex":"https://openalex.org/W7127328393","doi":"https://doi.org/10.48550/arxiv.2602.00665"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.00665","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120240485","display_name":"Lakshan Cooray","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cooray, Lakshan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094255504","display_name":"Deshan Sumanathilaka","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sumanathilaka, Deshan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124904669","display_name":"Pattigadapa Venkatesh Raju","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raju, Pattigadapa Venkatesh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"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":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":0.6292999982833862,"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":0.6292999982833862,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.05530000105500221,"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/T12128","display_name":"AI in Service Interactions","score":0.050599999725818634,"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/automatic-summarization","display_name":"Automatic summarization","score":0.8011999726295471},{"id":"https://openalex.org/keywords/conversation","display_name":"Conversation","score":0.6509000062942505},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5286999940872192},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4763999879360199},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4641000032424927},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.45190000534057617},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.41370001435279846},{"id":"https://openalex.org/keywords/computational-model","display_name":"Computational model","score":0.3716000020503998},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.36559998989105225}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.8011999726295471},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7943999767303467},{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.6509000062942505},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.52920001745224},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5286999940872192},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5009999871253967},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4763999879360199},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4641000032424927},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.45190000534057617},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.41370001435279846},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.3716000020503998},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.357699990272522},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.34529998898506165},{"id":"https://openalex.org/C3018587665","wikidata":"https://www.wikidata.org/wiki/Q7268696","display_name":"Qualitative analysis","level":3,"score":0.3334999978542328},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C190248442","wikidata":"https://www.wikidata.org/wiki/Q839486","display_name":"Qualitative research","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C2779313563","wikidata":"https://www.wikidata.org/wiki/Q17072565","display_name":"On Language","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.2639000117778778},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C2780876879","wikidata":"https://www.wikidata.org/wiki/Q3054749","display_name":"Meaning (existential)","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C129792486","wikidata":"https://www.wikidata.org/wiki/Q1050419","display_name":"Language identification","level":3,"score":0.25690001249313354}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.00665","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.00665","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.00665","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":"pmh:doi:10.48550/arxiv.2602.00665","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5030139684677124,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Customer-service":[0],"question":[1],"answering":[2],"(QA)":[3],"systems":[4],"increasingly":[5],"rely":[6],"on":[7],"conversational":[8,77],"language":[9,155],"understanding.":[10,58],"While":[11],"Large":[12],"Language":[13,33],"Models":[14,34],"(LLMs)":[15],"achieve":[16],"strong":[17],"performance,":[18,133],"their":[19,42],"high":[20],"computational":[21],"cost":[22],"and":[23,56,109,119,141,150],"deployment":[24],"constraints":[25],"limit":[26],"practical":[27],"use":[28],"in":[29,51],"resource-constrained":[30],"environments.":[31],"Small":[32],"(SLMs)":[35],"provide":[36],"a":[37,70,82],"more":[38],"efficient":[39],"alternative,":[40],"yet":[41],"effectiveness":[43],"for":[44,64,157],"multi-turn":[45,66],"customer-service":[46,67,95,159],"QA":[47,160],"remains":[48],"underexplored,":[49],"particularly":[50],"scenarios":[52],"requiring":[53],"dialogue":[54,139],"continuity":[55,140],"contextual":[57,142],"This":[59],"study":[60],"investigates":[61],"instruction-tuned":[62,98],"SLMs":[63,100],"context-summarized":[65],"QA,":[68],"using":[69,107],"history":[71],"summarization":[72],"strategy":[73],"to":[74,87,137],"preserve":[75],"essential":[76],"state.":[78],"We":[79],"also":[80],"introduce":[81],"conversation":[83],"stage-based":[84],"qualitative":[85,114],"analysis":[86],"evaluate":[88],"model":[89],"behavior":[90],"across":[91,126],"different":[92],"phases":[93],"of":[94,153],"interactions.":[96],"Nine":[97],"low-parameterized":[99,154],"are":[101],"evaluated":[102],"against":[103],"three":[104],"commercial":[105],"LLMs":[106],"lexical":[108],"semantic":[110],"similarity":[111],"metrics":[112],"alongside":[113],"assessments,":[115],"including":[116],"human":[117],"evaluation":[118],"LLM-as-a-judge":[120],"methods.":[121],"Results":[122],"show":[123],"notable":[124],"variation":[125],"SLMs,":[127],"with":[128],"some":[129],"models":[130,156],"demonstrating":[131],"near-LLM":[132],"while":[134],"others":[135],"struggle":[136],"maintain":[138],"alignment.":[143],"These":[144],"findings":[145],"highlight":[146],"both":[147],"the":[148],"potential":[149],"current":[151],"limitations":[152],"real-world":[158],"systems.":[161]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-04T00:00:00"}