{"id":"https://openalex.org/W6996730461","doi":null,"title":"Steerable Alignment with Conditional Multiobjective Preference Optimization","display_name":"Steerable Alignment with Conditional Multiobjective Preference Optimization","publication_year":2024,"publication_date":"2024-05-01","ids":{"openalex":"https://openalex.org/W6996730461"},"language":"en","primary_location":{"id":"pmh:oai:dspace.mit.edu:1721.1/156747","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/156747","pdf_url":"https://dspace.mit.edu/bitstream/1721.1/156747/1/manyika-jmanyika-meng-eecs-2024-thesis.pdf","source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://dspace.mit.edu/bitstream/1721.1/156747/1/manyika-jmanyika-meng-eecs-2024-thesis.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Manyika, Julian","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Manyika, Julian","raw_affiliation_strings":["MASTER OF ENGINEERING IN ELECTRICAL ENGINEERING AND COMPUTER SCIENCE","Massachusetts Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MASTER OF ENGINEERING IN ELECTRICAL ENGINEERING AND COMPUTER SCIENCE","institution_ids":[]},{"raw_affiliation_string":"Massachusetts Institute of Technology","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I63966007"],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.34619998931884766,"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":0.34619998931884766,"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":0.210099995136261,"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":0.052799999713897705,"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-model-alignment","display_name":"large language model alignment","score":0.9559999704360962},{"id":"https://openalex.org/keywords/direct-preference-optimization","display_name":"direct preference optimization","score":0.656000018119812},{"id":"https://openalex.org/keywords/pareto-front","display_name":"Pareto front","score":0.6600000262260437}],"concepts":[{"id":"https://openalex.org/C144251240","wikidata":"https://www.wikidata.org/wiki/Q7068229","display_name":"Nucleofection","level":4,"score":0.36550000309944153},{"id":"https://openalex.org/C2781032047","wikidata":"https://www.wikidata.org/wiki/Q938793","display_name":"Articular cartilage damage","level":5,"score":0.31299999356269836},{"id":"https://openalex.org/C2778283623","wikidata":"https://www.wikidata.org/wiki/Q18032200","display_name":"TSG101","level":5,"score":0.27239999175071716},{"id":"https://openalex.org/C145741570","wikidata":"https://www.wikidata.org/wiki/Q7251534","display_name":"Proteogenomics","level":5,"score":0.27230000495910645},{"id":"https://openalex.org/C180938184","wikidata":"https://www.wikidata.org/wiki/Q2142270","display_name":"Liquation","level":3,"score":0.26570001244544983},{"id":"https://openalex.org/C2777158700","wikidata":"https://www.wikidata.org/wiki/Q1419356","display_name":"Hyporeflexia","level":3,"score":0.25380000472068787},{"id":"https://openalex.org/C2992336715","wikidata":"https://www.wikidata.org/wiki/Q63431143","display_name":"Gestational period","level":4,"score":0.2459000051021576},{"id":"https://openalex.org/C18743360","wikidata":"https://www.wikidata.org/wiki/Q1208096","display_name":"Diafiltration","level":4,"score":0.2320999950170517},{"id":"https://openalex.org/C133074676","wikidata":"https://www.wikidata.org/wiki/Q428729","display_name":"Fusible alloy","level":2,"score":0.23010000586509705},{"id":"https://openalex.org/C2777054765","wikidata":"https://www.wikidata.org/wiki/Q6402731","display_name":"Dysgeusia","level":3,"score":0.22930000722408295}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:dspace.mit.edu:1721.1/156747","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/156747","pdf_url":"https://dspace.mit.edu/bitstream/1721.1/156747/1/manyika-jmanyika-meng-eecs-2024-thesis.pdf","source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"}],"best_oa_location":{"id":"pmh:oai:dspace.mit.edu:1721.1/156747","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/156747","pdf_url":"https://dspace.mit.edu/bitstream/1721.1/156747/1/manyika-jmanyika-meng-eecs-2024-thesis.pdf","source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W6996730461.pdf","grobid_xml":"https://content.openalex.org/works/W6996730461.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W3168867926","https://openalex.org/W4226112058","https://openalex.org/W4304195432","https://openalex.org/W4311991106","https://openalex.org/W4322718191","https://openalex.org/W4327810158","https://openalex.org/W4379089709","https://openalex.org/W4384918448","https://openalex.org/W4385436553","https://openalex.org/W4385571645","https://openalex.org/W4386437475","https://openalex.org/W4386875326","https://openalex.org/W4387892197","https://openalex.org/W4389520260","https://openalex.org/W4390632072","https://openalex.org/W4390963130","https://openalex.org/W4401042573","https://openalex.org/W4402684003","https://openalex.org/W7133199456","https://openalex.org/W7133208539","https://openalex.org/W7133224126"],"related_works":["https://openalex.org/W4405876239","https://openalex.org/W7162463546","https://openalex.org/W6979369794","https://openalex.org/W4412888500","https://openalex.org/W4389520260","https://openalex.org/W7140093270","https://openalex.org/W4398230715","https://openalex.org/W7166887977","https://openalex.org/W4402671571","https://openalex.org/W4399151740"],"abstract_inverted_index":{"As":[0],"the":[1,28,128,153],"scale,":[2],"capabilities":[3],"and":[4,88,124,139,146,174,192],"use-cases":[5],"of":[6,27,119,131],"large":[7],"language":[8],"models":[9,135,173,176],"(LLMs)":[10],"continue":[11],"to":[12,48,83,149,167],"grow,":[13],"it":[14],"is":[15,73],"imperative":[16],"that":[17,51,62,111,160,164,199],"these":[18,98],"systems":[19],"are":[20,52,64,165,200],"aligned":[21],"with":[22,55,141],"human":[23,56],"preferences.":[24,57,204],"Current":[25],"state":[26],"art":[29],"strategies":[30],"for":[31,45,76,91,196],"alignment":[32,109],"such":[33,121],"as":[34,122,189],"Reinforcement":[35],"Learning":[36],"from":[37,169,175,202],"Human":[38],"Feedback":[39],"(RLHF)":[40],"have":[41],"provided":[42],"useful":[43],"paradigms":[44],"finetuning":[46,140,194],"LLMs":[47,198],"produce":[49],"outputs":[50,151],"more":[53],"consistent":[54],"These":[58],"approaches,":[59],"however,":[60],"assume":[61],"preferences":[63],"formed":[65],"by":[66],"a":[67,107,113,180,190],"single,":[68],"underlying":[69],"reward":[70],"model,":[71],"which":[72],"likely":[74],"insufficient":[75],"representing":[77],"an":[78],"individual\u2019s":[79],"preferences,":[80,87],"certainly":[81],"unable":[82],"represent":[84],"diverse":[85],"group":[86],"inf":[89],"lexible":[90],"users":[92,148],"at":[93,156],"inference":[94],"time.":[95],"To":[96],"address":[97],"limitations,":[99],"we":[100],"propose":[101],"Conditional":[102],"Multiobjective":[103],"Preference":[104,143],"Optimization":[105],"(CMPO),":[106],"novel":[108],"strategy":[110,195],"trains":[112],"user-steerable":[114],"LLM":[115],"along":[116],"multiple":[117,132],"attributes":[118,155],"text,":[120],"helpfulness":[123],"humor.":[125],"CMPO":[126,161,185],"simulates":[127],"pareto":[129],"front":[130],"single-attribute":[133],"preference-optimized":[134],"through":[136],"structural":[137],"plurality":[138],"Direct":[142],"Optimzation":[144],"(DPO),":[145],"allows":[147],"condition":[150],"on":[152],"predefined":[154],"inference-time.":[157],"Experiments":[158],"show":[159],"generates":[162],"responses":[163],"preferred":[166],"those":[168],"separate":[170],"attribute-specific":[171],"DPO":[172],"trained":[177],"using":[178],"SteerLM,":[179],"alternate":[181],"model":[182],"steering":[183],"approach.":[184],"empirically":[186],"shows":[187],"promise":[188],"scalable":[191],"flexible":[193],"creating":[197],"attribute-steerable":[201],"parameterized":[203]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-10-10T00:00:00"}
