{"id":"https://openalex.org/W7154990167","doi":"https://doi.org/10.48550/arxiv.2604.15802","title":"CHOP: Chunkwise Context-Preserving Framework for RAG on Multi Documents","display_name":"CHOP: Chunkwise Context-Preserving Framework for RAG on Multi Documents","publication_year":2026,"publication_date":"2026-04-17","ids":{"openalex":"https://openalex.org/W7154990167","doi":"https://doi.org/10.48550/arxiv.2604.15802"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.15802","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.15802","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,"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.15802","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134088711","display_name":"Hyunseok Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Hyunseok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134079472","display_name":"Jihyeon Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Jihyeon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101418381","display_name":"Jongeun Kim","orcid":"https://orcid.org/0000-0003-3978-4650"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Jongeun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123039600","display_name":"Dongsik Yoon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoon, Dongsik","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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.6976000070571899,"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":0.6976000070571899,"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":0.1657000035047531,"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":0.014399999752640724,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.6132000088691711},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5842999815940857},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5605000257492065},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.527400016784668},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4099000096321106},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.40299999713897705},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.400299996137619}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7893999814987183},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.6132000088691711},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.589900016784668},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5842999815940857},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5605000257492065},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.527400016784668},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4377000033855438},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42149999737739563},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4099000096321106},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.40299999713897705},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.400299996137619},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.37630000710487366},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3375999927520752},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3294999897480011},{"id":"https://openalex.org/C89686163","wikidata":"https://www.wikidata.org/wiki/Q1187982","display_name":"Vector space model","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3034999966621399},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.257099986076355}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.15802","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.15802","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,"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":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.15802","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.15802","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,"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"},"sustainable_development_goals":[{"score":0.5349479913711548,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"},{"score":0.41837093234062195,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Retrieval-Augmented":[0],"Generation":[1],"(RAG)":[2],"systems":[3],"lose":[4],"retrieval":[5,120],"accuracy":[6],"when":[7],"similar":[8,106],"documents":[9,44,107],"coexist":[10],"in":[11,142],"the":[12,60,75,90],"vector":[13],"database,":[14],"causing":[15],"unnecessary":[16],"information,":[17],"hallucinations,":[18],"and":[19,41,71,74,108,122,139],"factual":[20],"errors.":[21],"To":[22],"alleviate":[23],"this":[24],"issue,":[25],"we":[26],"propose":[27],"CHOP,":[28],"a":[29,124,133],"framework":[30],"that":[31,117],"iteratively":[32],"evaluates":[33],"chunk":[34,97],"relevance":[35],"with":[36,49,98],"Large":[37],"Language":[38],"Models":[39],"(LLMs)":[40],"progressively":[42],"reconstructs":[43],"by":[45,83],"determining":[46],"their":[47],"association":[48],"specific":[50],"topics":[51],"or":[52],"query":[53],"types.":[54],"CHOP":[55,101,118],"integrates":[56],"two":[57],"key":[58,69],"components:":[59],"CNM-Extractor,":[61],"which":[62,79],"generates":[63],"compact":[64],"per-chunk":[65],"signatures":[66],"capturing":[67],"categories,":[68],"nouns,":[70],"model":[72],"names,":[73],"Continuity":[76],"Decision":[77],"Module,":[78],"preserves":[80],"contextual":[81],"coherence":[82],"deciding":[84],"whether":[85],"consecutive":[86],"chunks":[87],"belong":[88],"to":[89],"same":[91],"document":[92],"flow.":[93],"By":[94],"prefixing":[95],"each":[96],"context-aware":[99],"metadata,":[100],"reduces":[102],"semantic":[103],"conflicts":[104],"among":[105],"enhances":[109],"retriever":[110],"discrimination.":[111],"Experiments":[112],"on":[113],"benchmark":[114],"datasets":[115],"show":[116],"alleviates":[119],"confusion":[121],"provides":[123],"scalable":[125],"approach":[126],"for":[127],"building":[128],"high-quality":[129],"knowledge":[130],"bases,":[131],"achieving":[132],"Top-1":[134],"Hit":[135],"Rate":[136],"of":[137],"90.77%":[138],"notable":[140],"gains":[141],"ranking":[143],"quality":[144],"metrics.":[145]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-21T00:00:00"}