{"id":"https://openalex.org/W4402588200","doi":"https://doi.org/10.48550/arxiv.2407.17545","title":"Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning","display_name":"Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning","publication_year":2024,"publication_date":"2024-07-24","ids":{"openalex":"https://openalex.org/W4402588200","doi":"https://doi.org/10.48550/arxiv.2407.17545"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2407.17545","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2407.17545","pdf_url":"https://arxiv.org/pdf/2407.17545","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2407.17545","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050790932","display_name":"Hongwei Jin","orcid":"https://orcid.org/0000-0002-2851-595X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jin, Hongwei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016335226","display_name":"George Papadimitriou","orcid":"https://orcid.org/0000-0001-9384-5034"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Papadimitriou, George","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030055383","display_name":"R. Krishnan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raghavan, Krishnan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103142926","display_name":"Pawe\u0142 \u017buk","orcid":"https://orcid.org/0000-0001-5293-8517"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zuk, Pawel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068721920","display_name":"Prasanna Balaprakash","orcid":"https://orcid.org/0000-0002-0292-5715"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Balaprakash, Prasanna","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100390466","display_name":"Cong Wang","orcid":"https://orcid.org/0000-0002-2603-4626"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Cong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063480283","display_name":"Anirban Mandal","orcid":"https://orcid.org/0000-0001-5145-8618"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mandal, Anirban","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5031941569","display_name":"Ewa Deelman","orcid":"https://orcid.org/0000-0001-5106-503X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deelman, Ewa","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5050790932"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.984000027179718,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.984000027179718,"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/T11986","display_name":"Scientific Computing and Data Management","score":0.9722999930381775,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9573000073432922,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/anomaly-detection","display_name":"Anomaly detection","score":0.7939224243164062},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.7270839214324951},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6665442585945129},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6578020453453064},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5749961733818054},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5357891917228699},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4181995987892151},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3918474316596985},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09371539950370789},{"id":"https://openalex.org/keywords/archaeology","display_name":"Archaeology","score":0.07140696048736572}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7939224243164062},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.7270839214324951},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6665442585945129},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6578020453453064},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5749961733818054},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5357891917228699},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4181995987892151},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3918474316596985},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09371539950370789},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.07140696048736572},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2407.17545","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2407.17545","pdf_url":"https://arxiv.org/pdf/2407.17545","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2407.17545","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2407.17545","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2407.17545","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2407.17545","pdf_url":"https://arxiv.org/pdf/2407.17545","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4402588200.pdf","grobid_xml":"https://content.openalex.org/works/W4402588200.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,32,81,121],"in":[2,78,122],"computational":[3],"workflows":[4],"is":[5],"critical":[6],"for":[7,29,58,118],"ensuring":[8],"system":[9],"reliability":[10],"and":[11,64,74,94,97,101],"security.":[12],"However,":[13],"traditional":[14],"rule-based":[15],"methods":[16],"struggle":[17],"to":[18,37,61],"detect":[19],"novel":[20],"anomalies.":[21],"This":[22],"paper":[23,85],"leverages":[24],"large":[25],"language":[26],"models":[27],"(LLMs)":[28],"workflow":[30,110],"anomaly":[31,80,120],"by":[33],"exploiting":[34],"their":[35],"ability":[36],"learn":[38],"complex":[39,123],"data":[40,57],"patterns.":[41],"Two":[42],"approaches":[43],"are":[44,53],"investigated:":[45],"1)":[46],"supervised":[47],"fine-tuning":[48],"(SFT),":[49],"where":[50,69],"pre-trained":[51],"LLMs":[52,77,117],"fine-tuned":[54],"on":[55],"labeled":[56],"sentence":[59],"classification":[60],"identify":[62],"anomalies,":[63],"2)":[65],"in-context":[66],"learning":[67],"(ICL)":[68],"prompts":[70,100],"containing":[71],"task":[72],"descriptions":[73],"examples":[75],"guide":[76],"few-shot":[79,98],"without":[82],"fine-tuning.":[83],"The":[84],"evaluates":[86],"the":[87,113],"performance,":[88],"efficiency,":[89],"generalization":[90],"of":[91,116],"SFT":[92],"models,":[93],"explores":[95],"zero-shot":[96],"ICL":[99],"interpretability":[102],"enhancement":[103],"via":[104],"chain-of-thought":[105],"prompting.":[106],"Experiments":[107],"across":[108],"multiple":[109],"datasets":[111],"demonstrate":[112],"promising":[114],"potential":[115],"effective":[119],"executions.":[124]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}