Online Advertisements with LLMs: Opportunities and Challenges
This paper explores the potential for leveraging Large Language Models (LLM) in the realm of online advertising systems. We introduce a general framework for LLM advertisement, consisting of modification, bidding, prediction, and auction modules. Different design considerations for each module are p...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Feizi, Soheil Hajiaghayi, MohammadTaghi Rezaei, Keivan Shin, Suho |
| description | This paper explores the potential for leveraging Large Language Models (LLM)
in the realm of online advertising systems. We introduce a general framework
for LLM advertisement, consisting of modification, bidding, prediction, and
auction modules. Different design considerations for each module are presented.
These design choices are evaluated and discussed based on essential desiderata
required to maintain a sustainable system. Further fundamental questions
regarding practicality, efficiency, and implementation challenges are raised
for future research. Finally, we exposit how recent approaches on mechanism
design for LLM can be framed in our unified perspective. |
| doi_str_mv | 10.48550/arxiv.2311.07601 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2311_07601</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2311_07601</sourcerecordid><originalsourceid>FETCH-LOGICAL-a671-4d6a4359ec88916e8e421d560db029fcca5bb28bcb3c04e40c9013c5c0224b3f3</originalsourceid><addsrcrecordid>eNotj7tOwzAUQL0woJYPYMI_kHD9rFOJoYp4SamydI_8uKGWUhPFpsDfIwrT2Y7OIeSWQS2NUnBvl694rrlgrIaNBnZNHvo0xYR0F864lJjxhKlk-hnLkXbdPm9pP8_vS_lIsUTM1KZA26OdJkxvmNfkarRTxpt_rsjh6fHQvlRd__za7rrK6g2rZNBWCtWgN6ZhGg1KzoLSEBzwZvTeKue4cd4JDxIl-AaY8MoD59KJUazI3Z_20j_MSzzZ5Xv4_RguH-IHKZlCtQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Online Advertisements with LLMs: Opportunities and Challenges</title><source>arXiv.org</source><creator>Feizi, Soheil ; Hajiaghayi, MohammadTaghi ; Rezaei, Keivan ; Shin, Suho</creator><creatorcontrib>Feizi, Soheil ; Hajiaghayi, MohammadTaghi ; Rezaei, Keivan ; Shin, Suho</creatorcontrib><description>This paper explores the potential for leveraging Large Language Models (LLM)
in the realm of online advertising systems. We introduce a general framework
for LLM advertisement, consisting of modification, bidding, prediction, and
auction modules. Different design considerations for each module are presented.
These design choices are evaluated and discussed based on essential desiderata
required to maintain a sustainable system. Further fundamental questions
regarding practicality, efficiency, and implementation challenges are raised
for future research. Finally, we exposit how recent approaches on mechanism
design for LLM can be framed in our unified perspective.</description><identifier>DOI: 10.48550/arxiv.2311.07601</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computers and Society</subject><creationdate>2023-11</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2311.07601$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2311.07601$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Feizi, Soheil</creatorcontrib><creatorcontrib>Hajiaghayi, MohammadTaghi</creatorcontrib><creatorcontrib>Rezaei, Keivan</creatorcontrib><creatorcontrib>Shin, Suho</creatorcontrib><title>Online Advertisements with LLMs: Opportunities and Challenges</title><description>This paper explores the potential for leveraging Large Language Models (LLM)
in the realm of online advertising systems. We introduce a general framework
for LLM advertisement, consisting of modification, bidding, prediction, and
auction modules. Different design considerations for each module are presented.
These design choices are evaluated and discussed based on essential desiderata
required to maintain a sustainable system. Further fundamental questions
regarding practicality, efficiency, and implementation challenges are raised
for future research. Finally, we exposit how recent approaches on mechanism
design for LLM can be framed in our unified perspective.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Computers and Society</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj7tOwzAUQL0woJYPYMI_kHD9rFOJoYp4SamydI_8uKGWUhPFpsDfIwrT2Y7OIeSWQS2NUnBvl694rrlgrIaNBnZNHvo0xYR0F864lJjxhKlk-hnLkXbdPm9pP8_vS_lIsUTM1KZA26OdJkxvmNfkarRTxpt_rsjh6fHQvlRd__za7rrK6g2rZNBWCtWgN6ZhGg1KzoLSEBzwZvTeKue4cd4JDxIl-AaY8MoD59KJUazI3Z_20j_MSzzZ5Xv4_RguH-IHKZlCtQ</recordid><startdate>20231110</startdate><enddate>20231110</enddate><creator>Feizi, Soheil</creator><creator>Hajiaghayi, MohammadTaghi</creator><creator>Rezaei, Keivan</creator><creator>Shin, Suho</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20231110</creationdate><title>Online Advertisements with LLMs: Opportunities and Challenges</title><author>Feizi, Soheil ; Hajiaghayi, MohammadTaghi ; Rezaei, Keivan ; Shin, Suho</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a671-4d6a4359ec88916e8e421d560db029fcca5bb28bcb3c04e40c9013c5c0224b3f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Computers and Society</topic><toplevel>online_resources</toplevel><creatorcontrib>Feizi, Soheil</creatorcontrib><creatorcontrib>Hajiaghayi, MohammadTaghi</creatorcontrib><creatorcontrib>Rezaei, Keivan</creatorcontrib><creatorcontrib>Shin, Suho</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Feizi, Soheil</au><au>Hajiaghayi, MohammadTaghi</au><au>Rezaei, Keivan</au><au>Shin, Suho</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Online Advertisements with LLMs: Opportunities and Challenges</atitle><date>2023-11-10</date><risdate>2023</risdate><abstract>This paper explores the potential for leveraging Large Language Models (LLM)
in the realm of online advertising systems. We introduce a general framework
for LLM advertisement, consisting of modification, bidding, prediction, and
auction modules. Different design considerations for each module are presented.
These design choices are evaluated and discussed based on essential desiderata
required to maintain a sustainable system. Further fundamental questions
regarding practicality, efficiency, and implementation challenges are raised
for future research. Finally, we exposit how recent approaches on mechanism
design for LLM can be framed in our unified perspective.</abstract><doi>10.48550/arxiv.2311.07601</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.2311.07601 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_2311_07601 |
| source | arXiv.org |
| subjects | Computer Science - Artificial Intelligence Computer Science - Computers and Society |
| title | Online Advertisements with LLMs: Opportunities and Challenges |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-18T19%3A09%3A39IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Online%20Advertisements%20with%20LLMs:%20Opportunities%20and%20Challenges&rft.au=Feizi,%20Soheil&rft.date=2023-11-10&rft_id=info:doi/10.48550/arxiv.2311.07601&rft_dat=%3Carxiv_GOX%3E2311_07601%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |