{"id":"https://openalex.org/W4416034836","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.543","title":"CATCH: A Novel Data Synthesis Framework for High Therapy Fidelity and Memory-Driven Planning Chain of Thought in AI Counseling","display_name":"CATCH: A Novel Data Synthesis Framework for High Therapy Fidelity and Memory-Driven Planning Chain of Thought in AI Counseling","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416034836","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.543"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.543","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.543","pdf_url":"https://aclanthology.org/2025.findings-emnlp.543.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.543.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Mingyu Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mingyu Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101277478","display_name":"Jingkai Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jingkai Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120058636","display_name":"Zhaojie Chu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhaojie Chu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101395450","display_name":"Xiaofen Xing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaofen Xing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101534539","display_name":"Yirong Chen","orcid":"https://orcid.org/0000-0001-9004-4194"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yirong Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5007354180","display_name":"Xiangmin Xu","orcid":"https://orcid.org/0000-0003-4573-5820"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiangmin Xu","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":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"10254","last_page":"10286"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.04600000008940697,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.04600000008940697,"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/T12805","display_name":"Cognitive Science and Mapping","score":0.04360000044107437,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.041999999433755875,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/chain","display_name":"Chain (unit)","score":0.47760000824928284},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.42489999532699585},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.27140000462532043},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.26409998536109924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5393000245094299},{"id":"https://openalex.org/C199185054","wikidata":"https://www.wikidata.org/wiki/Q552299","display_name":"Chain (unit)","level":2,"score":0.47760000824928284},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.42489999532699585},{"id":"https://openalex.org/C195094911","wikidata":"https://www.wikidata.org/wiki/Q14167904","display_name":"Process management","level":1,"score":0.36399999260902405},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.34860000014305115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3352000117301941},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3107999861240387},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2957000136375427},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2906000018119812},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.543","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.543","pdf_url":"https://aclanthology.org/2025.findings-emnlp.543.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.543","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.543","pdf_url":"https://aclanthology.org/2025.findings-emnlp.543.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":[{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416034836.pdf","grobid_xml":"https://content.openalex.org/works/W4416034836.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recently,":[0],"advancements":[1],"in":[2,27,140],"AI":[3,141],"counseling":[4,87],"based":[5],"on":[6],"large":[7],"language":[8],"models":[9],"have":[10],"shown":[11],"significant":[12],"progress.However,":[13],"existing":[14],"studies":[15],"employ":[16],"a":[17,46,74,112],"one-time":[18],"generation":[19],"approach":[20],"to":[21,33,52,56,119,123],"synthesize":[22],"multi-turn":[23],"dialogue":[24,125],"samples,":[25],"resulting":[26],"low":[28],"therapy":[29,58],"fidelity":[30,136],"and":[31,71,82,109,128,137],"failing":[32],"capture":[34,89],"the":[35,62,97],"decision-making":[36,90],"rationale":[37,91],"behind":[38,92],"each":[39,93,124],"response.In":[40],"this":[41],"work,":[42],"we":[43,60,95],"propose":[44,96],"CATCH,":[45],"novel":[47],"data":[48],"synthesis":[49],"framework":[50],"designed":[51],"address":[53],"these":[54],"challenges.Specifically,":[55],"improve":[57],"fidelity,":[59],"introduce":[61],"Progressive":[63],"Dialogue":[64],"Synthesis":[65],"strategy,":[66],"which":[67],"extracts":[68],"goals,":[69],"resources,":[70],"solutions":[72],"from":[73],"client's":[75],"self-report,":[76],"organizes":[77],"them":[78],"into":[79],"structured":[80],"outlines,":[81],"then":[83,116],"incrementally":[84],"generates":[85],"stage-aligned":[86],"dialogues.To":[88],"response,":[94],"Memory-Driven":[98],"Dynamic":[99],"Planning":[100],"thinking":[101],"pattern":[102],"that":[103,132],"integrates":[104],"memory":[105],"enhancement,":[106],"global":[107],"planning,":[108],"strategy":[110],"reasoning;":[111],"collaborative":[113],"multi-agent":[114],"optimizer":[115],"leverages":[117],"MDP":[118],"attach":[120],"explicit":[121],"chain-of-thought":[122],"turn.Extensive":[126],"experiments":[127],"human":[129],"evaluations":[130],"demonstrate":[131],"CATCH":[133],"significantly":[134],"enhances":[135],"logical":[138],"coherence":[139],"counseling.":[142]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-08T00:00:00"}