[2304.11076] Can ChatGPT-like Generative Models Guarantee Factual Accuracy? On the Mistakes of New Generation Search Engines
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Computer Science > Computation and Language

arXiv:2304.11076 (cs)
[Submitted on 3 Mar 2023]

Title:Can ChatGPT-like Generative Models Guarantee Factual Accuracy? On the Mistakes of New Generation Search Engines

Authors:Ruochen Zhao, Xingxuan Li, Yew Ken Chia, Bosheng Ding, Lidong Bing
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Abstract:Although large conversational AI models such as OpenAI's ChatGPT have demonstrated great potential, we question whether such models can guarantee factual accuracy. Recently, technology companies such as Microsoft and Google have announced new services which aim to combine search engines with conversational AI. However, we have found numerous mistakes in the public demonstrations that suggest we should not easily trust the factual claims of the AI models. Rather than criticizing specific models or companies, we hope to call on researchers and developers to improve AI models' transparency and factual correctness.
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2304.11076 [cs.CL]
  (or arXiv:2304.11076v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2304.11076

Submission history

From: Xingxuan Li [view email]
[v1] Fri, 3 Mar 2023 04:27:44 UTC (10,277 KB)
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