{"id":"https://openalex.org/W4401863775","doi":"https://doi.org/10.1145/3637528.3671932","title":"Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models","display_name":"Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401863775","doi":"https://doi.org/10.1145/3637528.3671932"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671932","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671932","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102221256","display_name":"Zhibo Hu","orcid":"https://orcid.org/0009-0000-6120-3379"},"institutions":[{"id":"https://openalex.org/I1292875679","display_name":"Commonwealth Scientific and Industrial Research Organisation","ror":"https://ror.org/03qn8fb07","country_code":"AU","type":"government","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I4387156119"]},{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]},{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Zhibo Hu","raw_affiliation_strings":["The University of New South Wales & CSIRO Data61, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0009-0000-6120-3379","affiliations":[{"raw_affiliation_string":"The University of New South Wales & CSIRO Data61, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I1292875679","https://openalex.org/I31746571","https://openalex.org/I42894916"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100337560","display_name":"Chen Wang","orcid":"https://orcid.org/0000-0002-3119-4763"},"institutions":[{"id":"https://openalex.org/I1292875679","display_name":"Commonwealth Scientific and Industrial Research Organisation","ror":"https://ror.org/03qn8fb07","country_code":"AU","type":"government","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I4387156119"]},{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]},{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Chen Wang","raw_affiliation_strings":["CSIRO Data61 & The University of New South Wales, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-3119-4763","affiliations":[{"raw_affiliation_string":"CSIRO Data61 & The University of New South Wales, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I1292875679","https://openalex.org/I31746571","https://openalex.org/I42894916"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070864647","display_name":"Yanfeng Shu","orcid":null},"institutions":[{"id":"https://openalex.org/I1292875679","display_name":"Commonwealth Scientific and Industrial Research Organisation","ror":"https://ror.org/03qn8fb07","country_code":"AU","type":"government","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I4387156119"]},{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yanfeng Shu","raw_affiliation_strings":["CSIRO Data61, Hobart, Tasmania, Australia"],"raw_orcid":"https://orcid.org/0009-0002-1350-6847","affiliations":[{"raw_affiliation_string":"CSIRO Data61, Hobart, Tasmania, Australia","institution_ids":["https://openalex.org/I1292875679","https://openalex.org/I42894916"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018724341","display_name":"Hye-Young Paik","orcid":"https://orcid.org/0000-0003-4425-7388"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hye-Young Paik","raw_affiliation_strings":["University of New South Wales, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0003-4425-7388","affiliations":[{"raw_affiliation_string":"University of New South Wales, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064683660","display_name":"Liming Zhu","orcid":"https://orcid.org/0000-0001-5839-3765"},"institutions":[{"id":"https://openalex.org/I1292875679","display_name":"Commonwealth Scientific and Industrial Research Organisation","ror":"https://ror.org/03qn8fb07","country_code":"AU","type":"government","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I4387156119"]},{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]},{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Liming Zhu","raw_affiliation_strings":["CSIRO Data61 & University of New South Wales, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0001-5839-3765","affiliations":[{"raw_affiliation_string":"CSIRO Data61 & University of New South Wales, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I1292875679","https://openalex.org/I31746571","https://openalex.org/I42894916"]}]}],"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":16,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1119","last_page":"1130"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9994999766349792,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9890999794006348,"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/computer-science","display_name":"Computer science","score":0.7098084688186646},{"id":"https://openalex.org/keywords/perturbation","display_name":"Perturbation (astronomy)","score":0.4355929493904114},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.41024211049079895},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4099234938621521},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.15209001302719116},{"id":"https://openalex.org/keywords/astronomy","display_name":"Astronomy","score":0.10686519742012024}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7098084688186646},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.4355929493904114},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41024211049079895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4099234938621521},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.15209001302719116},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.10686519742012024}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637528.3671932","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671932","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":37,"referenced_works":["https://openalex.org/W1945616565","https://openalex.org/W2318810549","https://openalex.org/W2799194071","https://openalex.org/W2970290563","https://openalex.org/W2971970905","https://openalex.org/W3025408396","https://openalex.org/W3027879771","https://openalex.org/W3158360872","https://openalex.org/W3209540659","https://openalex.org/W4221143046","https://openalex.org/W4221158996","https://openalex.org/W4226082499","https://openalex.org/W4281657280","https://openalex.org/W4287332702","https://openalex.org/W4306309284","https://openalex.org/W4319049323","https://openalex.org/W4321854923","https://openalex.org/W4367832894","https://openalex.org/W4377372274","https://openalex.org/W4379958452","https://openalex.org/W4380267320","https://openalex.org/W4385374425","https://openalex.org/W4386302213","https://openalex.org/W4387156668","https://openalex.org/W4387725416","https://openalex.org/W4390963073","https://openalex.org/W6637162671","https://openalex.org/W6739901393","https://openalex.org/W6767057552","https://openalex.org/W6839328737","https://openalex.org/W6839394111","https://openalex.org/W6849760658","https://openalex.org/W6849898756","https://openalex.org/W6849941170","https://openalex.org/W6853859572","https://openalex.org/W6856800273","https://openalex.org/W6858023062"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W3204019825"],"abstract_inverted_index":{"The":[0],"robustness":[1,156],"of":[2,19,33,63,75,88,114,157,182],"large":[3],"language":[4],"models":[5],"(LLMs)":[6],"becomes":[7],"increasingly":[8],"important":[9],"as":[10,26],"their":[11],"use":[12],"rapidly":[13],"grows":[14],"in":[15,111,127],"a":[16,27,65,95,107,151,161],"wide":[17],"range":[18],"domains.":[20],"Retrieval-Augmented":[21],"Generation":[22],"(RAG)":[23],"is":[24,51],"considered":[25],"means":[28],"to":[29,68,72,117,131,149],"improve":[30],"the":[31,40,61,69,73,86,128,155,180],"trustworthiness":[32],"text":[34],"generation":[35,74],"from":[36,42,79],"LLMs.":[37],"However,":[38],"how":[39],"outputs":[41,76,113],"RAG-based":[43,115,158],"LLMs":[44,116,159,178],"are":[45],"affected":[46],"by":[47,93,170],"slightly":[48],"different":[49],"inputs":[50],"not":[52],"well":[53],"studied.":[54],"In":[55],"this":[56],"work,":[57],"we":[58],"find":[59],"that":[60,153],"insertion":[62],"even":[64],"short":[66],"prefix":[67],"prompt":[70],"leads":[71],"far":[77],"away":[78],"factually":[80],"correct":[81],"answers.":[82,120],"We":[83,135],"systematically":[84],"evaluate":[85],"effect":[87],"such":[89],"prefixes":[90],"on":[91,166,176],"RAG":[92],"introducing":[94],"novel":[96],"optimization":[97],"technique":[98],"called":[99],"Gradient":[100],"Guided":[101],"Prompt":[102],"Perturbation":[103],"(GGPP).":[104],"GGPP":[105,147,171],"achieves":[106],"high":[108],"success":[109],"rate":[110],"steering":[112],"targeted":[118],"wrong":[119],"It":[121],"can":[122],"also":[123,136],"cope":[124],"with":[125,144],"instructions":[126],"prompts":[129,143],"requesting":[130],"ignore":[132],"irrelevant":[133],"context.":[134],"exploit":[137],"LLMs'":[138],"neuron":[139,167],"activation":[140,168],"difference":[141],"between":[142],"and":[145],"without":[146],"perturbations":[148],"give":[150],"method":[152],"improves":[154],"through":[160],"highly":[162],"effective":[163],"detector":[164],"trained":[165],"triggered":[169],"generated":[172],"prompts.":[173],"Our":[174],"evaluation":[175],"open-sourced":[177],"demonstrates":[179],"effectiveness":[181],"our":[183],"methods.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}