{"id":"https://openalex.org/W7131836462","doi":"https://doi.org/10.1145/3794763.3794799","title":"EyeLayer: Integrating Human Attention Patterns into LLM-Based Code Summarization","display_name":"EyeLayer: Integrating Human Attention Patterns into LLM-Based Code Summarization","publication_year":2026,"publication_date":"2026-04-12","ids":{"openalex":"https://openalex.org/W7131836462","doi":"https://doi.org/10.1145/3794763.3794799"},"language":null,"primary_location":{"id":"doi:10.1145/3794763.3794799","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3794763.3794799","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 34th IEEE/ACM International Conference on Program Comprehension","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3794763.3794799","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127437657","display_name":"Jiahao Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiahao Zhang","raw_affiliation_strings":["Vanderbilt University, Nashville, USA"],"raw_orcid":"https://orcid.org/0009-0008-8379-6871","affiliations":[{"raw_affiliation_string":"Vanderbilt University, Nashville, USA","institution_ids":["https://openalex.org/I200719446"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127355859","display_name":"Yifan Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yifan Zhang","raw_affiliation_strings":["Vanderbilt University, Nashville, USA"],"raw_orcid":"https://orcid.org/0000-0001-5719-772X","affiliations":[{"raw_affiliation_string":"Vanderbilt University, Nashville, USA","institution_ids":["https://openalex.org/I200719446"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030030910","display_name":"Kevin Leach","orcid":"https://orcid.org/0000-0002-4001-3442"},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin Leach","raw_affiliation_strings":["Vanderbilt University, Nashville, USA"],"raw_orcid":"https://orcid.org/0000-0002-4001-3442","affiliations":[{"raw_affiliation_string":"Vanderbilt University, Nashville, USA","institution_ids":["https://openalex.org/I200719446"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5127184140","display_name":"Yu Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu Huang","raw_affiliation_strings":["Vanderbilt University, Nashville, USA"],"raw_orcid":"https://orcid.org/0000-0003-2730-5077","affiliations":[{"raw_affiliation_string":"Vanderbilt University, Nashville, USA","institution_ids":["https://openalex.org/I200719446"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I200719446"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19015012,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"61","last_page":"72"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.9427000284194946,"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/T10260","display_name":"Software Engineering Research","score":0.9427000284194946,"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":0.013000000268220901,"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/T10430","display_name":"Software Engineering Techniques and Practices","score":0.006200000178068876,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.7459999918937683},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5206999778747559},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4447999894618988},{"id":"https://openalex.org/keywords/natural-language-generation","display_name":"Natural language generation","score":0.43540000915527344},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.4339999854564667},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4293999969959259},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.42559999227523804},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.41609999537467957},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.4140999913215637}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7997000217437744},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.7459999918937683},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5206999778747559},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48989999294281006},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4853000044822693},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4447999894618988},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.43540000915527344},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.4339999854564667},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4293999969959259},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.42559999227523804},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.41609999537467957},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.4140999913215637},{"id":"https://openalex.org/C134537474","wikidata":"https://www.wikidata.org/wiki/Q17144832","display_name":"Naturalness","level":2,"score":0.34860000014305115},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33090001344680786},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.31470000743865967},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.28839999437332153},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2685000002384186},{"id":"https://openalex.org/C18552078","wikidata":"https://www.wikidata.org/wiki/Q255615","display_name":"Code-switching","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.25450000166893005},{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C2778828372","wikidata":"https://www.wikidata.org/wiki/Q5283209","display_name":"Distributional semantics","level":3,"score":0.25440001487731934},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C108154423","wikidata":"https://www.wikidata.org/wiki/Q1469792","display_name":"Salience (neuroscience)","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3794763.3794799","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3794763.3794799","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 34th IEEE/ACM International Conference on Program Comprehension","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2602.22368","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2602.22368","pdf_url":"https://arxiv.org/pdf/2602.22368","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3794763.3794799","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3794763.3794799","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 34th IEEE/ACM International Conference on Program Comprehension","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.849939227104187,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Code":[0],"summarization":[1],"is":[2,14],"the":[3,169],"task":[4],"of":[5,10,64,150,172],"generating":[6],"natural":[7],"language":[8,23],"descriptions":[9],"source":[11],"code,":[12],"which":[13],"critical":[15],"for":[16,180],"software":[17],"comprehension":[18],"and":[19,45,97,112,132,137,174],"maintenance.":[20],"While":[21],"large":[22],"models":[24,72,179],"(LLMs)":[25],"have":[26],"achieved":[27],"remarkable":[28],"progress":[29],"on":[30,87,154],"this":[31],"task,":[32],"an":[33],"open":[34],"question":[35],"remains:":[36],"can":[37],"human":[38,58,65,73,160],"expertise":[39],"in":[40],"code":[41,69,76,181],"understanding":[42],"further":[43],"guide":[44],"enhance":[46,168],"these":[47],"models?":[48],"We":[49,122],"propose":[50],"EyeLayer,":[51],"a":[52,62,79],"lightweight":[53],"attention-augmentation":[54],"module":[55],"that":[56,94,159,167],"incorporates":[57],"eye-gaze":[59],"patterns,":[60],"as":[61],"proxy":[63],"expertise,":[66],"into":[67,115],"LLM-based":[68],"summarization.":[70,182],"EyeLayer":[71,124,139],"attention":[74,107,165],"during":[75],"reading":[77],"via":[78],"Multimodal":[80],"Gaussian":[81],"Mixture,":[82],"redistributing":[83],"token":[84],"embeddings":[85],"based":[86],"learned":[88],"parameters":[89],"\\((\\mu":[90],"_i,":[91],"\\sigma":[92],"_i^2)\\)":[93],"capture":[95],"where":[96],"how":[98],"intensively":[99],"developers":[100],"focus.":[101],"This":[102],"design":[103],"enables":[104],"learning":[105],"generalizable":[106],"priors":[108],"from":[109],"eye-tracking":[110],"data":[111],"incorporating":[113],"them":[114],"LLMs":[116,173],"seamlessly,":[117],"without":[118],"disturbing":[119],"existing":[120],"representations.":[121],"evaluate":[123],"across":[125,145,177],"diverse":[126,178],"model":[127],"families":[128],"(i.e.,":[129],"LLaMA-3.2,":[130],"Qwen3,":[131],"CodeBERT)":[133],"covering":[134],"different":[135],"scales":[136],"architectures.":[138],"consistently":[140],"outperforms":[141],"strong":[142],"fine-tuning":[143],"baselines":[144],"standard":[146],"metrics,":[147],"achieving":[148],"gains":[149],"up":[151],"to":[152],"13.17%":[153],"BLEU-4.":[155],"These":[156],"results":[157],"demonstrate":[158],"gaze":[161],"patterns":[162],"encode":[163],"complementary":[164],"signals":[166],"semantic":[170],"focus":[171],"transfer":[175],"effectively":[176]},"counts_by_year":[],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2026-02-28T00:00:00"}
