{"id":"https://openalex.org/W7204188043","doi":"https://doi.org/10.18653/2026.findings-emnlp.1948","title":"The Emergence of Relevance Through Axiomatic Attention Patterns During LoRA Fine-Tuning","display_name":"The Emergence of Relevance Through Axiomatic Attention Patterns During LoRA Fine-Tuning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7204188043","doi":"https://doi.org/10.18653/2026.findings-emnlp.1948"},"language":"en","primary_location":{"id":"doi:10.18653/2026.findings-emnlp.1948","doi":"https://doi.org/10.18653/2026.findings-emnlp.1948","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.findings-emnlp.1948","pdf_url":"https://aclanthology.org/2026.findings-emnlp.1948.pdf","source":{"id":"https://openalex.org/S7407096085","display_name":"Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2026","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://aclanthology.org/2026.findings-emnlp.1948.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5148047778","display_name":"Matthew Perlman","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew Perlman","raw_affiliation_strings":["University of Massachusetts Amherst"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119079163","display_name":"Atharva Nijasure","orcid":"https://orcid.org/0009-0000-0997-1493","observed_orcids":["https://orcid.org/0009-0000-0997-1493"]},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Atharva Nijasure","raw_affiliation_strings":["University of Massachusetts Amherst"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5148050964","display_name":"James Allan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James Allan","raw_affiliation_strings":["University of Massachusetts Amherst"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst","institution_ids":["https://openalex.org/I24603500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24603500"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.59618454,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"41532","last_page":"41548"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9276999831199646,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9276999831199646,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.06729999929666519,"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":0.0007999999797903001,"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/low-rank-adaptation","display_name":"low-rank adaptation","score":0.9210000038146973},{"id":"https://openalex.org/keywords/re-ranking","display_name":"re-ranking","score":0.8220000267028809},{"id":"https://openalex.org/keywords/attention-patterns","display_name":"attention patterns","score":0.640999972820282},{"id":"https://openalex.org/keywords/relevance-learning","display_name":"relevance learning","score":0.5569999814033508},{"id":"https://openalex.org/keywords/interpretability","display_name":"interpretability","score":0.6129999756813049},{"id":"https://openalex.org/keywords/lexical-matching","display_name":"word matching","score":0.8100000023841858},{"id":"https://openalex.org/keywords/compositional-features","display_name":"compositional features","score":0.7950000166893005},{"id":"https://openalex.org/keywords/large-language-model-adaptation","display_name":"large language model adaptation","score":0.5590000152587891}],"concepts":[{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.8008999824523926},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6004999876022339},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5360000133514404},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5203999876976013},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.5052000284194946},{"id":"https://openalex.org/C167729594","wikidata":"https://www.wikidata.org/wiki/Q17736","display_name":"Axiom","level":2,"score":0.4864000082015991},{"id":"https://openalex.org/C125773388","wikidata":"https://www.wikidata.org/wiki/Q792542","display_name":"Axiomatic system","level":3,"score":0.4424000084400177},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41040000319480896},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3327000141143799},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3100999891757965},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.27059999108314514}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/2026.findings-emnlp.1948","doi":"https://doi.org/10.18653/2026.findings-emnlp.1948","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.findings-emnlp.1948","pdf_url":"https://aclanthology.org/2026.findings-emnlp.1948.pdf","source":{"id":"https://openalex.org/S7407096085","display_name":"Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2026","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2608.23338","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.23338","pdf_url":"https://arxiv.org/pdf/2608.23338","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.23338","doi":"https://doi.org/10.48550/arxiv.2608.23338","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.23338","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/2026.findings-emnlp.1948","doi":"https://doi.org/10.18653/2026.findings-emnlp.1948","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.findings-emnlp.1948","pdf_url":"https://aclanthology.org/2026.findings-emnlp.1948.pdf","source":{"id":"https://openalex.org/S7407096085","display_name":"Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204188043.pdf","grobid_xml":"https://content.openalex.org/works/W7204188043.grobid-xml"},"referenced_works_count":6,"referenced_works":["https://openalex.org/W2909544278","https://openalex.org/W2951534261","https://openalex.org/W4297412003","https://openalex.org/W4400525779","https://openalex.org/W4415338215","https://openalex.org/W4416172860"],"related_works":["https://openalex.org/W4416172860","https://openalex.org/W7131319267","https://openalex.org/W7157110526","https://openalex.org/W7108362331","https://openalex.org/W7155042577","https://openalex.org/W6979303653","https://openalex.org/W4414825089","https://openalex.org/W4408345888","https://openalex.org/W4416034389","https://openalex.org/W7125829007"],"abstract_inverted_index":{"LoRA":[0,36,65,72,92,119,162],"fine-tuning":[1,101,128,163],"is":[2,20,80],"standard":[3],"for":[4,82,169],"adapting":[5,170],"LLMs":[6],"to":[7,39,75,93,131],"reranking,":[8],"but":[9],"it":[10],"remains":[11],"unclear":[12],"where":[13,35,117,127],"in":[14,102,110,147],"the":[15,69,87,111,122],"network":[16],"task-specific":[17],"relevance":[18],"behavior":[19,159],"learned":[21],"and":[22,30,43,59,97,138,164],"what":[23],"attention-level":[24],"changes":[25],"accompany":[26],"that":[27,63,98,115],"learning.Through":[28],"ablation":[29],"attention":[31,37,51,73,95,100,130],"experiments,":[32],"we":[33,113],"identify":[34],"updates":[38,74],"RankLLaMA":[40],"improve":[41],"performance":[42,88,106,121],"whether":[44],"those":[45],"gains":[46,146],"coincide":[47],"with":[48,125,145],"interpretable":[49],"relevance-oriented":[50,158],"patterns":[52],"such":[53],"as":[54],"lexical":[55],"matching,":[56],"rarity":[57],"sensitivity,":[58,135],"query-document":[60],"interaction.We":[61],"find":[62],"given":[64],"fine-tuned":[66],"MLPs":[67],"throughout":[68],"network,":[70],"restricting":[71],"a":[76],"compact":[77],"mid-network":[78],"region":[79,104],"sufficient":[81],"recovering":[83],"over":[84],"half":[85],"of":[86,156],"gained":[89],"by":[90],"applying":[91,118],"all":[94],"layers,":[96],"omitting":[99],"this":[103],"hurts":[105],"more":[107],"than":[108],"elsewhere":[109],"network.Additionally,":[112],"show":[114],"regions":[116,126],"affects":[120],"most":[123],"overlap":[124],"increased":[129],"axiomatic":[132],"IR":[133],"features.Rarity":[134],"document-query":[136],"interaction,":[137],"several":[139],"compositional":[140],"features":[141],"are":[142],"highly":[143],"correlated":[144],"ranking":[148],"performance.Our":[149],"results":[150],"support":[151],"an":[152],"interpretable,":[153],"correlational":[154],"account":[155],"how":[157],"emerges":[160],"during":[161],"point":[165],"toward":[166],"improved":[167],"strategies":[168],"rerankers.":[171]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-08-26T00:00:00"}