{"id":"https://openalex.org/W4403789144","doi":"https://doi.org/10.48550/arxiv.2409.16418","title":"Task-oriented Prompt Enhancement via Script Generation","display_name":"Task-oriented Prompt Enhancement via Script Generation","publication_year":2024,"publication_date":"2024-09-24","ids":{"openalex":"https://openalex.org/W4403789144","doi":"https://doi.org/10.48550/arxiv.2409.16418"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2409.16418","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.16418","pdf_url":"https://arxiv.org/pdf/2409.16418","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"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2409.16418","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014705890","display_name":"Chung-Yu Wang","orcid":"https://orcid.org/0009-0006-4889-9504","observed_orcids":["https://orcid.org/0009-0006-4889-9504"]},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Wang, Chung-Yu","raw_affiliation_strings":["York University , Canada ALIREZA DAGHIGHFARSOODEH ,","York University , Toronto , Canada,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"York University , Canada ALIREZA DAGHIGHFARSOODEH ,","institution_ids":["https://openalex.org/I192455969"]},{"raw_affiliation_string":"York University , Toronto , Canada,","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114415547","display_name":"Alireza DaghighFarsoodeh","orcid":"https://orcid.org/0009-0007-8806-121X","observed_orcids":["https://orcid.org/0009-0007-8806-121X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"DaghighFarsoodeh, Alireza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5002648627","display_name":"Hung Viet Pham","orcid":"https://orcid.org/0000-0002-1769-8527","observed_orcids":["https://orcid.org/0000-0002-1769-8527","https://orcid.org/0000-0002-9016-0694"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pham, Hung Viet","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"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":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":0.9038000106811523,"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":0.9038000106811523,"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":0.040300000458955765,"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":0.012900000438094139,"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/prompt-engineering","display_name":"prompt engineering","score":0.7210000157356262},{"id":"https://openalex.org/keywords/script-generation","display_name":"script generation","score":0.8069999814033508},{"id":"https://openalex.org/keywords/code-generation","display_name":"code generation","score":0.6650000214576721},{"id":"https://openalex.org/keywords/zero-shot-learning","display_name":"zero-shot learning","score":0.8180000185966492},{"id":"https://openalex.org/keywords/chain-of-thought-prompting","display_name":"chain-of-thought prompting","score":0.9980000257492065},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.7390000224113464},{"id":"https://openalex.org/keywords/titan","display_name":"Titan","score":0.6079999804496765}],"concepts":[{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.667875349521637},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49829936027526855},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08626523613929749},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2409.16418","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.16418","pdf_url":"https://arxiv.org/pdf/2409.16418","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.2409.16418","doi":"https://doi.org/10.48550/arxiv.2409.16418","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2409.16418","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2409.16418","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.16418","pdf_url":"https://arxiv.org/pdf/2409.16418","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"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4403789144.pdf","grobid_xml":"https://content.openalex.org/works/W4403789144.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7172260499","https://openalex.org/W4416290380","https://openalex.org/W7081570326","https://openalex.org/W6998796428","https://openalex.org/W7117627197","https://openalex.org/W4402670307","https://openalex.org/W4415967301","https://openalex.org/W4417262486","https://openalex.org/W7162150309","https://openalex.org/W4392266005"],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"have":[4],"demonstrated":[5],"remarkable":[6],"abilities":[7],"across":[8],"various":[9,108],"tasks,":[10],"leveraging":[11],"advanced":[12],"reasoning.":[13],"Yet,":[14],"they":[15],"struggle":[16],"with":[17,198],"task-oriented":[18,73,241],"prompts":[19],"due":[20],"to":[21,40,68,129,139,148,167,220],"a":[22,82,118,179,236,244],"lack":[23],"of":[24,28,182,213],"specific":[25],"prior":[26],"knowledge":[27],"the":[29,93,131,150,157,163,169,188],"task":[30],"answers.":[31],"The":[32],"current":[33],"state-of-the-art":[34,189,208],"approach,":[35],"PAL,":[36],"utilizes":[37],"code":[38],"generation":[39],"address":[41],"this":[42,60,77],"issue.":[43],"However,":[44],"PAL":[45],"depends":[46],"on":[47,72,107,227],"manually":[48],"crafted":[49],"prompt":[50],"templates":[51],"and":[52,85,99,114,135,162,194,200],"examples":[53],"while":[54,216],"still":[55],"producing":[56],"inaccurate":[57],"results.":[58],"In":[59],"work,":[61],"we":[62],"present":[63],"TITAN-a":[64],"novel":[65,245],"strategy":[66],"designed":[67],"enhance":[69],"LLMs'":[70,105,151],"performance":[71,106,209],"prompts.":[74],"TITAN":[75,91,103,121,154,186,206],"achieves":[76,207],"by":[78,110,192,230],"generating":[79],"scripts":[80],"using":[81],"universal":[83],"approach":[84,191],"zero-shot":[86,190],"learning.":[87],"Unlike":[88],"existing":[89],"methods,":[90],"eliminates":[92],"need":[94],"for":[95,247],"detailed":[96],"task-specific":[97],"instructions":[98],"extensive":[100],"manual":[101],"efforts.":[102],"enhances":[104],"tasks":[109],"utilizing":[111,249],"their":[112],"analytical":[113],"code-generation":[115,152],"capabilities":[116],"in":[117,178,210,239,251],"streamlined":[119],"process.":[120,153],"employs":[122],"two":[123],"key":[124],"techniques:":[125],"(1)":[126],"step-back":[127],"prompting":[128,138],"extract":[130],"task's":[132],"input":[133],"specifications":[134],"(2)":[136],"chain-of-thought":[137],"identify":[140],"required":[141],"procedural":[142],"steps.":[143],"This":[144,233],"information":[145],"is":[146,165],"used":[147],"improve":[149],"further":[155],"refines":[156],"generated":[158],"script":[159,164],"through":[160],"post-processing":[161],"executed":[166],"retrieve":[168],"final":[170],"answer.":[171],"Our":[172],"comprehensive":[173],"evaluation":[174],"demonstrates":[175],"TITAN's":[176],"effectiveness":[177],"diverse":[180],"set":[181],"tasks.":[183,254],"On":[184],"average,":[185],"outperforms":[187],"7.6%":[193],"3.9%":[195],"when":[196],"paired":[197],"GPT-3.5":[199],"GPT-4.":[201],"Overall,":[202],"without":[203],"human":[204,225],"annotation,":[205],"8":[211],"out":[212],"11":[214],"cases":[215],"only":[217],"marginally":[218],"losing":[219],"few-shot":[221],"approaches":[222],"(which":[223],"needed":[224],"intervention)":[226],"three":[228],"occasions":[229],"small":[231],"margins.":[232],"work":[234],"represents":[235],"significant":[237],"advancement":[238],"addressing":[240],"prompts,":[242],"offering":[243],"solution":[246],"effectively":[248],"LLMs":[250],"everyday":[252],"life":[253]},"counts_by_year":[],"updated_date":"2026-10-08T09:06:37.294493","created_date":"2024-10-27T00:00:00"}
