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Synthetic Query Testing: Probing Assistants to Reverse-Engineer Citation Rules
Read the full article: Synthetic Query Testing: Probing Assistants to Reverse-Engineer Citation Rules
Discover more at Content Marketing Automation
Excerpt:
Introduction
Modern AI assistants (chatbots like ChatGPT or Bing Chat) often try to answer user questions and “show their work” by citing sources. However, studies show many answers have bad or missing citations. For example, Stanford researchers found that about half of AI chat answers contained unsupported statements or wrong citations (www.axios.com). In medical tests, new AI tools often gave answers not supported by the sources they cited (doaj.org). These problems mean we need better ways to test AI assistants’ citation behavior.
To understand how an AI picks what to cite, we propose a large-scale testing plan. We will create many synthetic queries (made-up questions) covering different subject areas and types of questions. We will run these through AI assistants automatically, gather their answers and citations, and label each cited source by its freshness (how recent), authority (how trusted), and structure (type or format). Then we use simple statistics to see which factors make it more likely an AI will cite a source. We will share all our data and tools openly. In this way, we can crowd-source improvements and keep monitoring AI citation behavior over time.
... Continue reading
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By AutoPod.coRead the full article: Synthetic Query Testing: Probing Assistants to Reverse-Engineer Citation Rules
Discover more at Content Marketing Automation
Excerpt:
Introduction
Modern AI assistants (chatbots like ChatGPT or Bing Chat) often try to answer user questions and “show their work” by citing sources. However, studies show many answers have bad or missing citations. For example, Stanford researchers found that about half of AI chat answers contained unsupported statements or wrong citations (www.axios.com). In medical tests, new AI tools often gave answers not supported by the sources they cited (doaj.org). These problems mean we need better ways to test AI assistants’ citation behavior.
To understand how an AI picks what to cite, we propose a large-scale testing plan. We will create many synthetic queries (made-up questions) covering different subject areas and types of questions. We will run these through AI assistants automatically, gather their answers and citations, and label each cited source by its freshness (how recent), authority (how trusted), and structure (type or format). Then we use simple statistics to see which factors make it more likely an AI will cite a source. We will share all our data and tools openly. In this way, we can crowd-source improvements and keep monitoring AI citation behavior over time.
... Continue reading