{"id":"https://openalex.org/W4415917196","doi":"https://doi.org/10.48550/arxiv.2510.27206","title":"Fints: Efficient Inference-Time Personalization for LLMs with Fine-Grained Instance-Tailored Steering","display_name":"Fints: Efficient Inference-Time Personalization for LLMs with Fine-Grained Instance-Tailored Steering","publication_year":2025,"publication_date":"2025-10-31","ids":{"openalex":"https://openalex.org/W4415917196","doi":"https://doi.org/10.48550/arxiv.2510.27206"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2510.27206","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.27206","pdf_url":"https://arxiv.org/pdf/2510.27206","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.27206","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074618552","display_name":"Kounianhua Du","orcid":"https://orcid.org/0000-0002-2611-5055","observed_orcids":["https://orcid.org/0000-0002-2611-5055"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Kounianhua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106670276","display_name":"Jianxing Liu","orcid":"https://orcid.org/0000-0002-2201-3887","observed_orcids":["https://orcid.org/0000-0002-2201-3887","https://orcid.org/0000-0003-0050-2548","https://orcid.org/0000-0001-6209-6886","https://orcid.org/0000-0002-2836-7651","https://orcid.org/0000-0003-2965-3518","https://orcid.org/0009-0006-2307-4530","https://orcid.org/0009-0001-1194-3333","https://orcid.org/0009-0005-5098-4452"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jianxing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhang, Kangning","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Kangning","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023250445","display_name":"Wenxiang Jiao","orcid":"https://orcid.org/0000-0003-4951-9420","observed_orcids":["https://orcid.org/0000-0003-4951-9420"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiao, Wenxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Lu, Yuan","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035163745","display_name":"Jiarui Jin","orcid":"https://orcid.org/0000-0001-6458-1586","observed_orcids":["https://orcid.org/0000-0001-6458-1586","https://orcid.org/0009-0001-2955-5371"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Jiarui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101500385","display_name":"Weiwen Liu","orcid":"https://orcid.org/0000-0002-9148-3997","observed_orcids":["https://orcid.org/0000-0002-9148-3997"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Weiwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100927581","display_name":"Yong Yu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Yong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5090720315","display_name":"Weinan Zhang","orcid":"https://orcid.org/0000-0002-0127-2425","observed_orcids":["https://orcid.org/0000-0002-0127-2425"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Weinan","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.47609999775886536,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.47609999775886536,"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/T10028","display_name":"Topic Modeling","score":0.3337000012397766,"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":0.03290000185370445,"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/llm-personalization","display_name":"LLM personalization","score":0.8230000138282776},{"id":"https://openalex.org/keywords/data-sparsity","display_name":"data sparsity","score":0.5559999942779541},{"id":"https://openalex.org/keywords/reward-modeling","display_name":"reward modeling","score":0.5889999866485596},{"id":"https://openalex.org/keywords/text-generation","display_name":"text generation","score":0.5609999895095825}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.8585000038146973},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.7759000062942505},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7329000234603882},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6090999841690063},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5637000203132629},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5339999794960022},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.39399999380111694},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C67712803","wikidata":"https://www.wikidata.org/wiki/Q7901853","display_name":"User modeling","level":3,"score":0.3571999967098236},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.33959999680519104},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C170130773","wikidata":"https://www.wikidata.org/wiki/Q216378","display_name":"Usability","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C47487241","wikidata":"https://www.wikidata.org/wiki/Q5227230","display_name":"Data access","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.2784999907016754},{"id":"https://openalex.org/C118643609","wikidata":"https://www.wikidata.org/wiki/Q189210","display_name":"Web application","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2590999901294708},{"id":"https://openalex.org/C2775941552","wikidata":"https://www.wikidata.org/wiki/Q25212305","display_name":"Isolation (microbiology)","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25450000166893005},{"id":"https://openalex.org/C2779965156","wikidata":"https://www.wikidata.org/wiki/Q5227350","display_name":"Data sharing","level":3,"score":0.2540000081062317},{"id":"https://openalex.org/C89505385","wikidata":"https://www.wikidata.org/wiki/Q47146","display_name":"User interface","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2510.27206","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.27206","pdf_url":"https://arxiv.org/pdf/2510.27206","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.27206","doi":"https://doi.org/10.48550/arxiv.2510.27206","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.27206","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.27206","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.27206","pdf_url":"https://arxiv.org/pdf/2510.27206","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7221779681","https://openalex.org/W7214791226","https://openalex.org/W7160963526","https://openalex.org/W6979371308","https://openalex.org/W7211901556","https://openalex.org/W4412945092","https://openalex.org/W4404782947","https://openalex.org/W7160859086","https://openalex.org/W7124460297","https://openalex.org/W7211907036"],"abstract_inverted_index":{"The":[0,138],"rapid":[1],"evolution":[2],"of":[3,34,192],"large":[4],"language":[5],"models":[6],"(LLMs)":[7],"has":[8],"intensified":[9],"the":[10,26,30,95,157,188],"demand":[11],"for":[12,99],"effective":[13],"personalization":[14,174,202],"techniques":[15],"that":[16,82,113,130,197],"can":[17],"adapt":[18],"model":[19],"behavior":[20],"to":[21,64,162],"individual":[22],"user":[23,56,89],"preferences.":[24],"Despite":[25],"non-parametric":[27],"methods":[28,50,164],"utilizing":[29],"in-context":[31],"learning":[32],"ability":[33],"LLMs,":[35],"recent":[36],"parametric":[37],"adaptation":[38],"methods,":[39],"including":[40],"personalized":[41,100],"parameter-efficient":[42],"fine-tuning":[43],"and":[44,58,67,78,91,122,125,143,150,165,184,190,214],"reward":[45],"modeling":[46],"emerge.":[47],"However,":[48],"these":[49,72,132],"face":[51],"limitations":[52],"in":[53,147,204],"handling":[54],"dynamic":[55],"patterns":[57],"high":[59,141,151],"data":[60,68,90,144,152],"sparsity":[61,153],"scenarios,":[62],"due":[63],"low":[65],"adaptability":[66],"efficiency.":[69],"To":[70],"address":[71],"challenges,":[73],"we":[74],"propose":[75],"a":[76,109,168],"fine-grained":[77,110],"instance-tailored":[79],"steering":[80,111],"framework":[81],"dynamically":[83],"generates":[84],"sample-level":[85],"interference":[86],"vectors":[87],"from":[88,120],"injects":[92],"them":[93],"into":[94,134],"model's":[96],"forward":[97],"pass":[98],"adaptation.":[101],"Our":[102],"approach":[103],"introduces":[104],"two":[105],"key":[106],"technical":[107],"innovations:":[108],"component":[112,170],"captures":[114],"nuanced":[115],"signals":[116,133],"by":[117],"hooking":[118],"activations":[119],"attention":[121],"MLP":[123],"layers,":[124],"an":[126],"input-aware":[127],"aggregation":[128],"module":[129],"synthesizes":[131],"contextually":[135],"relevant":[136],"enhancements.":[137],"method":[139,159,199],"demonstrates":[140],"flexibility":[142],"efficiency,":[145],"excelling":[146],"fast-changing":[148],"distribution":[149],"scenarios.":[154],"In":[155],"addition,":[156],"proposed":[158],"is":[160,218],"orthogonal":[161],"existing":[163],"operates":[166],"as":[167],"plug-in":[169],"compatible":[171],"with":[172],"different":[173],"techniques.":[175],"Extensive":[176],"experiments":[177],"across":[178,210],"diverse":[179],"scenarios--including":[180],"short-to-long":[181],"text":[182],"generation,":[183],"web":[185],"function":[186],"calling--validate":[187],"effectiveness":[189],"compatibility":[191],"our":[193,198],"approach.":[194],"Results":[195],"show":[196],"significantly":[200],"enhances":[201],"performance":[203],"fast-shifting":[205],"environments":[206],"while":[207],"maintaining":[208],"robustness":[209],"varying":[211],"interaction":[212],"modes":[213],"context":[215],"lengths.":[216],"Implementation":[217],"available":[219],"at":[220],"https://github.com/KounianhuaDu/Fints.":[221]},"counts_by_year":[],"updated_date":"2026-11-01T11:40:01.658920","created_date":"2025-11-05T00:00:00"}