{"id":"https://openalex.org/W7140133219","doi":"https://doi.org/10.48550/arxiv.2603.19294","title":"Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data","display_name":"Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data","publication_year":2026,"publication_date":"2026-03-10","ids":{"openalex":"https://openalex.org/W7140133219","doi":"https://doi.org/10.48550/arxiv.2603.19294"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.19294","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19294","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":null,"license_id":null,"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.2603.19294","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130372945","display_name":"Hyunji Nam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nam, Hyunji","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130401279","display_name":"Haoran Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Haoran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5046953322","display_name":"Natasha Jaques","orcid":"https://orcid.org/0000-0002-8413-9469"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jaques, Natasha","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/T10028","display_name":"Topic Modeling","score":0.38019999861717224,"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":0.38019999861717224,"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":0.06210000067949295,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.05640000104904175,"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/personalization","display_name":"Personalization","score":0.7541999816894531},{"id":"https://openalex.org/keywords/pointwise-mutual-information","display_name":"Pointwise mutual information","score":0.5638999938964844},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5414000153541565},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.4961000084877014},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.46059998869895935},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.3840999901294708}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.7541999816894531},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7121000289916992},{"id":"https://openalex.org/C7797323","wikidata":"https://www.wikidata.org/wiki/Q3798612","display_name":"Pointwise mutual information","level":3,"score":0.5638999938964844},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5414000153541565},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.4961000084877014},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.46059998869895935},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37720000743865967},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3734000027179718},{"id":"https://openalex.org/C2777984123","wikidata":"https://www.wikidata.org/wiki/Q9248237","display_name":"Pointwise","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C124805900","wikidata":"https://www.wikidata.org/wiki/Q5159269","display_name":"Conditional mutual information","level":3,"score":0.3528999984264374},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3456000089645386},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2644999921321869}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.19294","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19294","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.19294","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19294","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.8113932609558105,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"post-training":[1],"has":[2,26],"successfully":[3],"improved":[4],"large":[5],"language":[6],"models":[7,139],"(LLMs)":[8],"across":[9],"a":[10,74,85,94,100,186],"variety":[11],"of":[12],"domains,":[13,165],"these":[14],"gains":[15,145,175],"heavily":[16],"rely":[17],"on":[18,54,89,99,151],"human-labeled":[19],"data":[20,25,32,76,116,179,197],"or":[21,180],"external":[22,55,181],"verifiers.":[23],"Existing":[24],"already":[27],"been":[28],"exploited,":[29],"and":[30,57,64,93,127,136,169],"new":[31],"is":[33],"expensive":[34],"to":[35,61,111,154],"collect.":[36],"Moreover,":[37],"true":[38],"intelligence":[39],"goes":[40],"far":[41],"beyond":[42],"verifiable":[43,63,164],"tasks.":[44],"Therefore,":[45],"we":[46],"need":[47],"self-improvement":[48,190],"frameworks":[49],"that":[50,79,106,141],"are":[51],"less":[52],"dependent":[53],"signals":[56,193],"more":[58],"broadly":[59],"applicable":[60],"both":[62],"non-verifiable":[65],"domains.":[66],"We":[67,104],"propose":[68],"**Mutual":[69],"Information":[70],"Preference":[71,109],"Optimization":[72,110],"(MIPO)**,":[73],"contrastive":[75,196],"augmentation":[77],"method":[78],"constructs":[80],"preference":[81],"pairs":[82],"by":[83,97],"generating":[84],"positive":[86],"response":[87,96],"conditioning":[88,98],"the":[90,122],"correct":[91],"prompt,":[92],"negative":[95],"random,":[101],"unrelated":[102],"prompt.":[103],"show":[105,140],"using":[107,191],"Direct":[108],"learn":[112],"from":[113,195],"this":[114],"paired":[115],"maximizes":[117],"pointwise":[118],"mutual":[119],"information":[120],"*under":[121],"base":[123],"LLM*":[124],"between":[125],"prompts":[126],"model":[128],"responses.":[129],"Experiments":[130],"with":[131,132],"1-7B":[133],"parameter":[134],"Llama":[135],"Qwen":[137],"instruct":[138],"MIPO":[142,158],"achieves":[143],"3-16%":[144],"(and":[146],"51%":[147],"increase":[148],"for":[149,189],"Qwen2.5-1.5B-Instruct)":[150],"personalization":[152],"compared":[153],"prompting":[155],"baselines.":[156],"Surprisingly,":[157],"can":[159],"also":[160],"be":[161],"useful":[162],"in":[163],"such":[166],"as":[167],"math":[168],"multiple-choice":[170],"question":[171],"answering,":[172],"yielding":[173],"1-20%":[174],"*without":[176],"any":[177],"additional":[178],"supervision*.":[182],"These":[183],"results":[184],"suggest":[185],"promising":[187],"direction":[188],"intrinsic":[192],"derived":[194],"pairs.":[198]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-24T00:00:00"}