{"id":"https://openalex.org/W4416035894","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.220","title":"ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation","display_name":"ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416035894","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.220"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.220","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.220","pdf_url":"https://aclanthology.org/2025.findings-emnlp.220.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.findings-emnlp.220.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113255600","display_name":"Ruobing Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruobing Yao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100386935","display_name":"Yifei Zhang","orcid":"https://orcid.org/0000-0002-4928-2921"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yifei Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100731558","display_name":"Shuang Song","orcid":"https://orcid.org/0000-0003-3262-7898"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuang Song","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001346139","display_name":"Yuhan Liu","orcid":"https://orcid.org/0000-0003-3576-6666"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuhan Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072074260","display_name":"Neng Gao","orcid":"https://orcid.org/0000-0002-0870-5692"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Neng Gao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5086465211","display_name":"Chenyang Tu","orcid":"https://orcid.org/0000-0003-2130-0531"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chenyang Tu","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":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4137","last_page":"4151"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.5346999764442444,"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":0.5346999764442444,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.26019999384880066,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.060600001364946365,"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/key","display_name":"Key (lock)","score":0.26499998569488525},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.25940001010894775},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.2563999891281128},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.2498999983072281},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.24969999492168427}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.602400004863739},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35589998960494995},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25940001010894775},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2498999983072281},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.24969999492168427},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.24950000643730164},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.24009999632835388},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2386000007390976}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.220","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.220","pdf_url":"https://aclanthology.org/2025.findings-emnlp.220.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.220","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.220","pdf_url":"https://aclanthology.org/2025.findings-emnlp.220.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416035894.pdf","grobid_xml":"https://content.openalex.org/works/W4416035894.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"Retrieval-Augmented":[1],"Generation":[2],"systems":[3,38],"enhance":[4,120],"Large":[5],"Language":[6],"Models":[7],"by":[8,43],"incorporating":[9],"external":[10],"knowledge,":[11],"they":[12],"still":[13],"face":[14],"persistent":[15],"challenges":[16],"in":[17,64],"retrieval":[18,65,135],"inefficiency":[19],"and":[20,51,67,98,116,128],"the":[21,44,82],"inability":[22],"of":[23,81],"LLMs":[24],"to":[25,85,119],"filter":[26],"out":[27],"irrelevant":[28],"information.We":[29],"present":[30],"ParetoRAG,":[31],"an":[32],"unsupervised":[33],"framework":[34,89],"that":[35,102],"optimizes":[36],"RAG":[37,87],"through":[39],"sentence-level":[40],"refinement":[41],"guided":[42],"Pareto":[45],"principle.By":[46],"decomposing":[47],"paragraphs":[48],"into":[49],"sentences":[50],"dynamically":[52],"reweighting":[53],"core":[54],"content":[55],"while":[56,77],"preserving":[57],"contextual":[58],"coherence,":[59],"ParetoRAG":[60],"achieves":[61],"dual":[62],"improvements":[63,105],"precision":[66],"generation":[68,121],"quality":[69,122],"without":[70],"requiring":[71],"additional":[72],"training":[73,118],"or":[74],"API":[75],"resources,":[76],"using":[78],"only":[79],"40%":[80],"tokens":[83],"compared":[84],"traditional":[86],"approaches.This":[88],"has":[90],"been":[91],"empirically":[92],"validated":[93],"across":[94],"various":[95],"datasets,":[96],"LLMs,":[97],"retrievers.Furthermore,":[99],"we":[100],"show":[101],"ParetoRAG's":[103],"architectural":[104,126],"are":[106],"orthogonally":[107],"compatible":[108],"with":[109,137],"adaptive":[110],"noiserobust":[111],"models,":[112],"enabling":[113],"retrieval-augmented":[114],"optimization":[115],"robust":[117],"mutually.This":[123],"highlights":[124],"complementary":[125],"refinements":[127],"noise":[129],"mitigation,":[130],"offering":[131],"insights":[132],"for":[133],"integrating":[134],"augmentation":[136],"robustness":[138],"enhancement.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-08T00:00:00"}
