{"id":"https://openalex.org/W4416677766","doi":"https://doi.org/10.1109/dsaa65442.2025.11248029","title":"Abstention is all you need","display_name":"Abstention is all you need","publication_year":2025,"publication_date":"2025-10-09","ids":{"openalex":"https://openalex.org/W4416677766","doi":"https://doi.org/10.1109/dsaa65442.2025.11248029"},"language":null,"primary_location":{"id":"doi:10.1109/dsaa65442.2025.11248029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa65442.2025.11248029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 12th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120474316","display_name":"Erik Sch\u00f6nw\u00e4lder","orcid":null},"institutions":[{"id":"https://openalex.org/I78650965","display_name":"TU Dresden","ror":"https://ror.org/042aqky30","country_code":"DE","type":"education","lineage":["https://openalex.org/I78650965"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Erik Sch\u00f6nw\u00e4lder","raw_affiliation_strings":["Technische Universit&#x00E4;t Dresden,Database Research Group,Dresden,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universit&#x00E4;t Dresden,Database Research Group,Dresden,Germany","institution_ids":["https://openalex.org/I78650965"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000427929","display_name":"Christian Falkenberg","orcid":null},"institutions":[{"id":"https://openalex.org/I78650965","display_name":"TU Dresden","ror":"https://ror.org/042aqky30","country_code":"DE","type":"education","lineage":["https://openalex.org/I78650965"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christian Falkenberg","raw_affiliation_strings":["Technische Universit&#x00E4;t Dresden,Database Research Group,Dresden,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universit&#x00E4;t Dresden,Database Research Group,Dresden,Germany","institution_ids":["https://openalex.org/I78650965"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034533320","display_name":"Claudio Hartmann","orcid":"https://orcid.org/0000-0002-5334-059X"},"institutions":[{"id":"https://openalex.org/I78650965","display_name":"TU Dresden","ror":"https://ror.org/042aqky30","country_code":"DE","type":"education","lineage":["https://openalex.org/I78650965"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Claudio Hartmann","raw_affiliation_strings":["Technische Universit&#x00E4;t Dresden,Database Research Group,Dresden,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universit&#x00E4;t Dresden,Database Research Group,Dresden,Germany","institution_ids":["https://openalex.org/I78650965"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063512642","display_name":"Wolfgang Lehner","orcid":"https://orcid.org/0000-0001-8107-2775"},"institutions":[{"id":"https://openalex.org/I78650965","display_name":"TU Dresden","ror":"https://ror.org/042aqky30","country_code":"DE","type":"education","lineage":["https://openalex.org/I78650965"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wolfgang Lehner","raw_affiliation_strings":["Technische Universit&#x00E4;t Dresden,Database Research Group,Dresden,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universit&#x00E4;t Dresden,Database Research Group,Dresden,Germany","institution_ids":["https://openalex.org/I78650965"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5120474316"],"corresponding_institution_ids":["https://openalex.org/I78650965"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.32198411,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.2930999994277954,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.2930999994277954,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.17960000038146973,"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/T10028","display_name":"Topic Modeling","score":0.1559000015258789,"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/action","display_name":"Action (physics)","score":0.29089999198913574},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2741999924182892},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.2703000009059906},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.26460000872612}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5810999870300293},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.4300999939441681},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3393999934196472},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3181999921798706},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2985000014305115},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.2460000067949295}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa65442.2025.11248029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa65442.2025.11248029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 12th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2108325777","https://openalex.org/W2144211451","https://openalex.org/W2147947791","https://openalex.org/W2522957395","https://openalex.org/W2963339397","https://openalex.org/W2963748441","https://openalex.org/W2997591727","https://openalex.org/W3035441651","https://openalex.org/W3108753913","https://openalex.org/W3162587184","https://openalex.org/W3173618889","https://openalex.org/W3183048323","https://openalex.org/W3183516990","https://openalex.org/W3199958362","https://openalex.org/W4283792888","https://openalex.org/W4285146641","https://openalex.org/W4285298351","https://openalex.org/W4382318384","https://openalex.org/W4389519963","https://openalex.org/W4389520749","https://openalex.org/W4402671653","https://openalex.org/W4412158322"],"related_works":[],"abstract_inverted_index":{"Despite":[0],"their":[1],"outstanding":[2],"performance":[3],"across":[4],"various":[5],"NLP":[6],"tasks,":[7],"Large":[8],"Language":[9],"Models":[10],"(LLMs)":[11],"still":[12],"produce":[13],"incorrect":[14],"answers,":[15],"which":[16],"can":[17],"be":[18,45],"harmful":[19],"in":[20],"safety-critical":[21],"domains":[22],"like":[23],"medicine":[24],"and":[25,102],"autonomous":[26],"driving.":[27],"To":[28],"address":[29],"this":[30],"issue,":[31],"selective":[32,77],"prediction":[33,78],"systems":[34],"aim":[35],"to":[36,44,60],"reject":[37],"predictions":[38],"from":[39],"LLMs":[40],"that":[41,80],"are":[42],"likely":[43],"incorrect.":[46],"However,":[47],"current":[48,107],"approaches":[49],"either":[50],"rely":[51],"on":[52,105],"querying":[53],"the":[54,67,84,106,111],"LLM":[55,85,112],"multiple":[56],"times,":[57],"requiring":[58,122],"access":[59],"its":[61,119],"internals,":[62],"or":[63,121],"fine-tuning":[64],"it.":[65],"Given":[66],"significant":[68],"operational":[69],"costs":[70],"of":[71],"an":[72,90],"LLM,":[73],"we":[74],"propose":[75],"a":[76,114],"system":[79],"does":[81],"not":[82],"involve":[83],"during":[86],"inference.":[87],"We":[88],"conduct":[89],"extensive":[91],"experimental":[92],"study":[93],"regarding":[94],"training":[95],"data":[96],"sizes,":[97],"time":[98],"consumption,":[99],"utilized":[100],"models,":[101],"embeddings,":[103],"improving":[104],"state-of-the-art":[108],"while":[109],"treating":[110],"as":[113],"black":[115],"box,":[116],"without":[117],"accessing":[118],"internals":[120],"fine-tuning.":[123]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-25T00:00:00"}
