{"id":"https://openalex.org/W7204904339","doi":"https://doi.org/10.18653/2026.emnlp-industry.255","title":"Reading the News: Adapting Large Language Models to Swedish Journalism Through Continued Pre-Training","display_name":"Reading the News: Adapting Large Language Models to Swedish Journalism Through Continued Pre-Training","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7204904339","doi":"https://doi.org/10.18653/2026.emnlp-industry.255"},"language":"en","primary_location":{"id":"doi:10.18653/2026.emnlp-industry.255","doi":"https://doi.org/10.18653/2026.emnlp-industry.255","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-industry.255","pdf_url":"https://aclanthology.org/2026.emnlp-industry.255.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://aclanthology.org/2026.emnlp-industry.255.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079473111","display_name":"Lukas Borggren","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I102134673","display_name":"Link\u00f6ping University","ror":"https://ror.org/05ynxx418","country_code":"SE","type":"education","lineage":["https://openalex.org/I102134673"]},{"id":"https://openalex.org/I4210099896","display_name":"Bonnier (Sweden)","ror":"https://ror.org/015m1y186","country_code":"SE","type":"company","lineage":["https://openalex.org/I4210099896"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Lukas Borggren","raw_affiliation_strings":["Bonnier News","Link\u00f6ping University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bonnier News","institution_ids":["https://openalex.org/I4210099896"]},{"raw_affiliation_string":"Link\u00f6ping University","institution_ids":["https://openalex.org/I102134673"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049592538","display_name":"Jenny Kunz","orcid":"https://orcid.org/0009-0006-1001-0546","observed_orcids":["https://orcid.org/0009-0006-1001-0546"]},"institutions":[{"id":"https://openalex.org/I102134673","display_name":"Link\u00f6ping University","ror":"https://ror.org/05ynxx418","country_code":"SE","type":"education","lineage":["https://openalex.org/I102134673"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Jenny Kunz","raw_affiliation_strings":["Link\u00f6ping University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Link\u00f6ping University","institution_ids":["https://openalex.org/I102134673"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069485198","display_name":"Marco Kuhlmann","orcid":"https://orcid.org/0000-0002-2492-9872","observed_orcids":["https://orcid.org/0000-0002-2492-9872"]},"institutions":[{"id":"https://openalex.org/I102134673","display_name":"Link\u00f6ping University","ror":"https://ror.org/05ynxx418","country_code":"SE","type":"education","lineage":["https://openalex.org/I102134673"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Marco Kuhlmann","raw_affiliation_strings":["Link\u00f6ping University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Link\u00f6ping University","institution_ids":["https://openalex.org/I102134673"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.69319568,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4305","last_page":"4327"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9740999937057495,"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":0.9740999937057495,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.014600000344216824,"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":0.00839999970048666,"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/large-language-models","display_name":"large language models","score":0.8659999966621399},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"domain adaptation","score":0.7319999933242798},{"id":"https://openalex.org/keywords/continual-pre-training","display_name":"continual pre-training","score":0.968999981880188},{"id":"https://openalex.org/keywords/swedish-journalism","display_name":"Swedish journalism","score":0.949999988079071},{"id":"https://openalex.org/keywords/news-articles","display_name":"news articles","score":0.9240000247955322},{"id":"https://openalex.org/keywords/experience-replay","display_name":"experience replay","score":0.722000002861023},{"id":"https://openalex.org/keywords/catastrophic-forgetting","display_name":"catastrophic forgetting","score":0.9430000185966492},{"id":"https://openalex.org/keywords/low-rank-adaptation","display_name":"low-rank adaptation","score":0.8759999871253967},{"id":"https://openalex.org/keywords/parameter-efficient-fine-tuning","display_name":"parameter-efficient fine-tuning","score":0.8420000076293945},{"id":"https://openalex.org/keywords/instruction-following","display_name":"instruction following","score":0.699999988079071},{"id":"https://openalex.org/keywords/factual-knowledge","display_name":"factual knowledge","score":0.7919999957084656},{"id":"https://openalex.org/keywords/news-summarization","display_name":"news summarization","score":0.32499998807907104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6984999775886536},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6822999715805054},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6819000244140625},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6728000044822693},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6150000095367432},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5397999882698059},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.5005000233650208},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4578999876976013},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.45410001277923584},{"id":"https://openalex.org/C119513131","wikidata":"https://www.wikidata.org/wiki/Q11030","display_name":"Journalism","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3312000036239624},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32260000705718994},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3068000078201294},{"id":"https://openalex.org/C2779236577","wikidata":"https://www.wikidata.org/wiki/Q5421970","display_name":"Extensive reading","level":3,"score":0.29409998655319214},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C74672266","wikidata":"https://www.wikidata.org/wiki/Q815859","display_name":"Language acquisition","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.2597000002861023}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/2026.emnlp-industry.255","doi":"https://doi.org/10.18653/2026.emnlp-industry.255","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-industry.255","pdf_url":"https://aclanthology.org/2026.emnlp-industry.255.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2608.30609","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.30609","pdf_url":"https://arxiv.org/pdf/2608.30609","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.2608.30609","doi":"https://doi.org/10.48550/arxiv.2608.30609","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.30609","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.emnlp-industry.255","doi":"https://doi.org/10.18653/2026.emnlp-industry.255","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-industry.255","pdf_url":"https://aclanthology.org/2026.emnlp-industry.255.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track","raw_type":"proceedings-article"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"score":0.6525171399116516,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322327","display_name":"Knut och Alice Wallenbergs Stiftelse","ror":"https://ror.org/004hzzk67"},{"id":"https://openalex.org/F4320334322","display_name":"HORIZON EUROPE Framework Programme","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204904339.pdf","grobid_xml":"https://content.openalex.org/works/W7204904339.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3017961061","https://openalex.org/W4386875765","https://openalex.org/W3211745141","https://openalex.org/W7082281435","https://openalex.org/W4389524115","https://openalex.org/W4390962117","https://openalex.org/W7064576408","https://openalex.org/W4415658888","https://openalex.org/W7152620927","https://openalex.org/W3173870405"],"abstract_inverted_index":{"Large":[0],"language":[1,45],"models":[2,28,46,139],"are":[3],"increasingly":[4],"capable":[5],"in":[6,14,93,110,122,151],"general,":[7],"but":[8,97,118,136],"their":[9,120],"utility":[10],"can":[11],"remain":[12],"modest":[13],"niche":[15],"or":[16],"understudied":[17],"areas.One":[18],"approach":[19],"to":[20,25,47,104,128,162],"address":[21],"this":[22,35],"limitation":[23],"is":[24],"specialise":[26],"existing":[27,157],"through":[29],"additional":[30],"training":[31],"on":[32],"target-domain":[33],"corpora.In":[34],"work,":[36],"we":[37,55,66,86,132,144],"investigate":[38],"such":[39],"continued":[40,89],"pre-training":[41,90],"for":[42,138],"adapting":[43],"large":[44],"Swedish":[48,158],"journalism,":[49],"using":[50],"a":[51,69,125],"high-quality":[52],"dataset":[53],"that":[54,73,88],"curate":[56],"from":[57],"millions":[58],"of":[59,148],"news":[60],"articles.To":[61],"evaluate":[62],"the":[63,94,111,146,152,164],"adaptation":[64,153],"efficacy,":[65],"also":[67],"construct":[68],"novel":[70],"domain-specific":[71],"benchmark":[72,159],"covers":[74],"six":[75],"editorial":[76],"tasks.Through":[77],"full":[78],"and":[79,115],"parameterefficient":[80],"fine-tuning":[81],"across":[82],"two":[83],"model":[84],"sizes,":[85],"find":[87],"yields":[91],"benefits":[92],"target":[95],"domain,":[96],"only":[98],"when":[99],"paired":[100],"with":[101,141],"experience":[102],"replay":[103],"mitigate":[105],"forgetting.We":[106],"observe":[107],"consistent":[108],"enhancements":[109],"models'":[112,165],"generation":[113],"quality":[114],"factual":[116],"knowledge,":[117],"not":[119],"proficiency":[121],"discriminative":[123],"tasks.Exploring":[124],"training-free":[126],"method":[127],"facilitate":[129],"instruction":[130],"following,":[131],"see":[133],"further":[134],"improvements,":[135],"exclusively":[137],"trained":[140],"low-rank":[142],"adaptation.Crucially,":[143],"demonstrate":[145],"importance":[147],"targeted":[149],"evaluation":[150],"process,":[154],"as":[155],"an":[156],"largely":[160],"fails":[161],"capture":[163],"indomain":[166],"performance":[167],"gains.":[168]},"counts_by_year":[],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2026-09-02T00:00:00"}
