ReadProbe: A Demo of Retrieval-Enhanced Large Language Models to Support Lateral Reading
With the rapid growth and spread of online misinformation, people need tools to help them evaluate the credibility and accuracy of online information. Lateral reading, a strategy that involves cross-referencing information with multiple sources, may be an effective approach to achieving this goal. I...
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| creator | Zhang, Dake Pradeep, Ronak |
| description | With the rapid growth and spread of online misinformation, people need tools
to help them evaluate the credibility and accuracy of online information.
Lateral reading, a strategy that involves cross-referencing information with
multiple sources, may be an effective approach to achieving this goal. In this
paper, we present ReadProbe, a tool to support lateral reading, powered by
generative large language models from OpenAI and the Bing search engine. Our
tool is able to generate useful questions for lateral reading, scour the web
for relevant documents, and generate well-attributed answers to help people
better evaluate online information. We made a web-based application to
demonstrate how ReadProbe can help reduce the risk of being misled by false
information. The code is available at
https://github.com/DakeZhang1998/ReadProbe. An earlier version of our tool won
the first prize in a national AI misinformation hackathon. |
| doi_str_mv | 10.48550/arxiv.2306.07875 |
| format | Article |
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to help them evaluate the credibility and accuracy of online information.
Lateral reading, a strategy that involves cross-referencing information with
multiple sources, may be an effective approach to achieving this goal. In this
paper, we present ReadProbe, a tool to support lateral reading, powered by
generative large language models from OpenAI and the Bing search engine. Our
tool is able to generate useful questions for lateral reading, scour the web
for relevant documents, and generate well-attributed answers to help people
better evaluate online information. We made a web-based application to
demonstrate how ReadProbe can help reduce the risk of being misled by false
information. The code is available at
https://github.com/DakeZhang1998/ReadProbe. An earlier version of our tool won
the first prize in a national AI misinformation hackathon.</description><identifier>DOI: 10.48550/arxiv.2306.07875</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Human-Computer Interaction ; Computer Science - Information Retrieval</subject><creationdate>2023-06</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>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,777,882</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2306.07875$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2306.07875$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Dake</creatorcontrib><creatorcontrib>Pradeep, Ronak</creatorcontrib><title>ReadProbe: A Demo of Retrieval-Enhanced Large Language Models to Support Lateral Reading</title><description>With the rapid growth and spread of online misinformation, people need tools
to help them evaluate the credibility and accuracy of online information.
Lateral reading, a strategy that involves cross-referencing information with
multiple sources, may be an effective approach to achieving this goal. In this
paper, we present ReadProbe, a tool to support lateral reading, powered by
generative large language models from OpenAI and the Bing search engine. Our
tool is able to generate useful questions for lateral reading, scour the web
for relevant documents, and generate well-attributed answers to help people
better evaluate online information. We made a web-based application to
demonstrate how ReadProbe can help reduce the risk of being misled by false
information. The code is available at
https://github.com/DakeZhang1998/ReadProbe. An earlier version of our tool won
the first prize in a national AI misinformation hackathon.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Computation and Language</subject><subject>Computer Science - Human-Computer Interaction</subject><subject>Computer Science - Information Retrieval</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tOwzAURL1hgQofwAr_QILfsdlVpTykIFDpgl10E1-HSGkcuWkFf09a2MyMNNKRDiE3nOXKas3uIH13x1xIZnJW2EJfks8Ngn9PscZ7uqQPuIs0BrrBKXV4hD5bD18wNOhpCanFOYf2APN4jR77PZ0i_TiMY0zTfE2YoKcnYDe0V-QiQL_H6_9ekO3jert6zsq3p5fVsszAFDpzWAdrtG548BKZD1JwLrhqFAvOKtYw62oQvigCt9I5LmvmhHLe1Mpwz-SC3P5hz2rVmLodpJ_qpFidFeUv-m5KnA</recordid><startdate>20230613</startdate><enddate>20230613</enddate><creator>Zhang, Dake</creator><creator>Pradeep, Ronak</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20230613</creationdate><title>ReadProbe: A Demo of Retrieval-Enhanced Large Language Models to Support Lateral Reading</title><author>Zhang, Dake ; Pradeep, Ronak</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a675-9ebf8655c1fd3e0df3211214c40f9840c089ba2d77f1839913b09249d6b461d03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Computation and Language</topic><topic>Computer Science - Human-Computer Interaction</topic><topic>Computer Science - Information Retrieval</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Dake</creatorcontrib><creatorcontrib>Pradeep, Ronak</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhang, Dake</au><au>Pradeep, Ronak</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ReadProbe: A Demo of Retrieval-Enhanced Large Language Models to Support Lateral Reading</atitle><date>2023-06-13</date><risdate>2023</risdate><abstract>With the rapid growth and spread of online misinformation, people need tools
to help them evaluate the credibility and accuracy of online information.
Lateral reading, a strategy that involves cross-referencing information with
multiple sources, may be an effective approach to achieving this goal. In this
paper, we present ReadProbe, a tool to support lateral reading, powered by
generative large language models from OpenAI and the Bing search engine. Our
tool is able to generate useful questions for lateral reading, scour the web
for relevant documents, and generate well-attributed answers to help people
better evaluate online information. We made a web-based application to
demonstrate how ReadProbe can help reduce the risk of being misled by false
information. The code is available at
https://github.com/DakeZhang1998/ReadProbe. An earlier version of our tool won
the first prize in a national AI misinformation hackathon.</abstract><doi>10.48550/arxiv.2306.07875</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Human-Computer Interaction Computer Science - Information Retrieval |
| title | ReadProbe: A Demo of Retrieval-Enhanced Large Language Models to Support Lateral Reading |
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