BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage
We present BlenderBot 3, a 175B parameter dialogue model capable of open-domain conversation with access to the internet and a long-term memory, and having been trained on a large number of user defined tasks. We release both the model weights and code, and have also deployed the model on a public w...
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creator | Shuster, Kurt Xu, Jing Komeili, Mojtaba Ju, Da Smith, Eric Michael Roller, Stephen Ung, Megan Chen, Moya Arora, Kushal Lane, Joshua Behrooz, Morteza Ngan, William Poff, Spencer Goyal, Naman Szlam, Arthur Boureau, Y-Lan Kambadur, Melanie Weston, Jason |
description | We present BlenderBot 3, a 175B parameter dialogue model capable of
open-domain conversation with access to the internet and a long-term memory,
and having been trained on a large number of user defined tasks. We release
both the model weights and code, and have also deployed the model on a public
web page to interact with organic users. This technical report describes how
the model was built (architecture, model and training scheme), and details of
its deployment, including safety mechanisms. Human evaluations show its
superiority to existing open-domain dialogue agents, including its predecessors
(Roller et al., 2021; Komeili et al., 2022). Finally, we detail our plan for
continual learning using the data collected from deployment, which will also be
publicly released. The goal of this research program is thus to enable the
community to study ever-improving responsible agents that learn through
interaction. |
doi_str_mv | 10.48550/arxiv.2208.03188 |
format | Article |
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open-domain conversation with access to the internet and a long-term memory,
and having been trained on a large number of user defined tasks. We release
both the model weights and code, and have also deployed the model on a public
web page to interact with organic users. This technical report describes how
the model was built (architecture, model and training scheme), and details of
its deployment, including safety mechanisms. Human evaluations show its
superiority to existing open-domain dialogue agents, including its predecessors
(Roller et al., 2021; Komeili et al., 2022). Finally, we detail our plan for
continual learning using the data collected from deployment, which will also be
publicly released. The goal of this research program is thus to enable the
community to study ever-improving responsible agents that learn through
interaction.</description><identifier>DOI: 10.48550/arxiv.2208.03188</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computation and Language</subject><creationdate>2022-08</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>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2208.03188$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2208.03188$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Shuster, Kurt</creatorcontrib><creatorcontrib>Xu, Jing</creatorcontrib><creatorcontrib>Komeili, Mojtaba</creatorcontrib><creatorcontrib>Ju, Da</creatorcontrib><creatorcontrib>Smith, Eric Michael</creatorcontrib><creatorcontrib>Roller, Stephen</creatorcontrib><creatorcontrib>Ung, Megan</creatorcontrib><creatorcontrib>Chen, Moya</creatorcontrib><creatorcontrib>Arora, Kushal</creatorcontrib><creatorcontrib>Lane, Joshua</creatorcontrib><creatorcontrib>Behrooz, Morteza</creatorcontrib><creatorcontrib>Ngan, William</creatorcontrib><creatorcontrib>Poff, Spencer</creatorcontrib><creatorcontrib>Goyal, Naman</creatorcontrib><creatorcontrib>Szlam, Arthur</creatorcontrib><creatorcontrib>Boureau, Y-Lan</creatorcontrib><creatorcontrib>Kambadur, Melanie</creatorcontrib><creatorcontrib>Weston, Jason</creatorcontrib><title>BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage</title><description>We present BlenderBot 3, a 175B parameter dialogue model capable of
open-domain conversation with access to the internet and a long-term memory,
and having been trained on a large number of user defined tasks. We release
both the model weights and code, and have also deployed the model on a public
web page to interact with organic users. This technical report describes how
the model was built (architecture, model and training scheme), and details of
its deployment, including safety mechanisms. Human evaluations show its
superiority to existing open-domain dialogue agents, including its predecessors
(Roller et al., 2021; Komeili et al., 2022). Finally, we detail our plan for
continual learning using the data collected from deployment, which will also be
publicly released. The goal of this research program is thus to enable the
community to study ever-improving responsible agents that learn through
interaction.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Computation and Language</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tOwzAURL1hgQofwAr_QIIfCXHY0YqXVIlNdyyiG_veEsnYkW0q8ve0hdVIM0cjHcZupKgb07biDtLPdKiVEqYWWhpzyT7WHoPDtI6F6wcO3OHs44KO2xgOmDKUKQbwHPYYCi-fUE5LmcI3eL9wj5BC5iXyhHmOIU_jscWwP_JX7ILAZ7z-zxXbPT_tNq_V9v3lbfO4reC-MxUCKeusBaCerGklgVMtNdo1YHWnRWcc6RElgqKxtyTREBhUou-VMqNesdu_27PdMKfpC9IynCyHs6X-BfthUBE</recordid><startdate>20220805</startdate><enddate>20220805</enddate><creator>Shuster, Kurt</creator><creator>Xu, Jing</creator><creator>Komeili, Mojtaba</creator><creator>Ju, Da</creator><creator>Smith, Eric Michael</creator><creator>Roller, Stephen</creator><creator>Ung, Megan</creator><creator>Chen, Moya</creator><creator>Arora, Kushal</creator><creator>Lane, Joshua</creator><creator>Behrooz, Morteza</creator><creator>Ngan, William</creator><creator>Poff, Spencer</creator><creator>Goyal, Naman</creator><creator>Szlam, Arthur</creator><creator>Boureau, Y-Lan</creator><creator>Kambadur, Melanie</creator><creator>Weston, Jason</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20220805</creationdate><title>BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage</title><author>Shuster, Kurt ; Xu, Jing ; Komeili, Mojtaba ; Ju, Da ; Smith, Eric Michael ; Roller, Stephen ; Ung, Megan ; Chen, Moya ; Arora, Kushal ; Lane, Joshua ; Behrooz, Morteza ; Ngan, William ; Poff, Spencer ; Goyal, Naman ; Szlam, Arthur ; Boureau, Y-Lan ; Kambadur, Melanie ; Weston, Jason</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a678-eaf2cdccaaf9fc851fad25f43d4ac373078df3be1ea2fb9cf1e8fa8e2099228b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Computation and Language</topic><toplevel>online_resources</toplevel><creatorcontrib>Shuster, Kurt</creatorcontrib><creatorcontrib>Xu, Jing</creatorcontrib><creatorcontrib>Komeili, Mojtaba</creatorcontrib><creatorcontrib>Ju, Da</creatorcontrib><creatorcontrib>Smith, Eric Michael</creatorcontrib><creatorcontrib>Roller, Stephen</creatorcontrib><creatorcontrib>Ung, Megan</creatorcontrib><creatorcontrib>Chen, Moya</creatorcontrib><creatorcontrib>Arora, Kushal</creatorcontrib><creatorcontrib>Lane, Joshua</creatorcontrib><creatorcontrib>Behrooz, Morteza</creatorcontrib><creatorcontrib>Ngan, William</creatorcontrib><creatorcontrib>Poff, Spencer</creatorcontrib><creatorcontrib>Goyal, Naman</creatorcontrib><creatorcontrib>Szlam, Arthur</creatorcontrib><creatorcontrib>Boureau, Y-Lan</creatorcontrib><creatorcontrib>Kambadur, Melanie</creatorcontrib><creatorcontrib>Weston, Jason</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shuster, Kurt</au><au>Xu, Jing</au><au>Komeili, Mojtaba</au><au>Ju, Da</au><au>Smith, Eric Michael</au><au>Roller, Stephen</au><au>Ung, Megan</au><au>Chen, Moya</au><au>Arora, Kushal</au><au>Lane, Joshua</au><au>Behrooz, Morteza</au><au>Ngan, William</au><au>Poff, Spencer</au><au>Goyal, Naman</au><au>Szlam, Arthur</au><au>Boureau, Y-Lan</au><au>Kambadur, Melanie</au><au>Weston, Jason</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage</atitle><date>2022-08-05</date><risdate>2022</risdate><abstract>We present BlenderBot 3, a 175B parameter dialogue model capable of
open-domain conversation with access to the internet and a long-term memory,
and having been trained on a large number of user defined tasks. We release
both the model weights and code, and have also deployed the model on a public
web page to interact with organic users. This technical report describes how
the model was built (architecture, model and training scheme), and details of
its deployment, including safety mechanisms. Human evaluations show its
superiority to existing open-domain dialogue agents, including its predecessors
(Roller et al., 2021; Komeili et al., 2022). Finally, we detail our plan for
continual learning using the data collected from deployment, which will also be
publicly released. The goal of this research program is thus to enable the
community to study ever-improving responsible agents that learn through
interaction.</abstract><doi>10.48550/arxiv.2208.03188</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Artificial Intelligence Computer Science - Computation and Language |
title | BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage |
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