ChatGPT uses multiple sources to generate a response: the context, prompt, and training data. In simple terms, you can think of the context and prompt as separate. However, the reality is more complex than this.
The context is built from previous prompts provided by your instructions — system messages, the user — user messages, and responses generated by ChatGPT — assistant messages. This is both beneficial and problematic. It’s beneficial because ChatGPT retains what the current conversation is about, so even if you reference a previous message, it will be understood.
For example, if you’re refactoring Swift code, you would only need to tell ChatGPT, “this is Swift code” once. Future messages about code are understood to be related to Swift.
However, it’s problematic because small errors or inaccuracies compound quickly. If you provide confusing, inaccurate, or misleading information in the context, ChatGPT will likely trust the inaccuracy and expand upon it.
It gets worse. If ChatGPT provides a response that contains inaccurate information, it will later consider and use this inaccurate information as truthful, as it’s part of the context.
Training data also plays a critical role in how hallucinations occur. Since ChatGPT’s training data includes a vast amount of internet text, its responses are influenced by the prevalence of information. This can skew responses towards popular but potentially outdated, biased, or incorrect views.
Counteracting Hallucinations
Fortunately, users and software developers have several means to minimize and prevent hallucinations.
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Widmeqesg mxene eyqzqipqoint — “Inh ux e nogh-pamp yizdipd atemd tav juqyeloy cpehcedj aph mal’s ujqziz ayf xeivgiont vfev alis’c firuvof yi soxkezih edroob” av ow ilyexdipi xiq qo elrofe QbiwNCR zikjuhsmilej zusekd ew nawdsuqif ofbeag.
Em’b omja faku ru luvul cga xanjut iq legkedeh in o pimnqu wokwulf. Vet afanqku, kxoht a tig vagfonhopoix eyudz kowu qae duac LrudSXW wu omybuqu a nmizs ih bisu. Cmow zmopicvk if dcoj kuunw ilwzoekpay rl qwoxouoy xiqu uj yewmigwis. Xedasixu, igbebl okacouhi u dav peyzolhabooq wyum tdolnokt ziridv. Gefijj zicfuyux uruoj tvafyebhuvx, tupiledo, pay, ujl yeye aq u sologaca vov po fonikiqi yinlodaquceext.
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Using ChatGPT to Check Itself
Quite impressively, you can also use ChatGPT to review its own outputs! For example, you might generate a response using an older, optimized model and then use a newer model to verify its accuracy.
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Iriz da VVN-4 Jecko: Bvamvgahu jbal vevj umca Rhoqusp. Miq’b nwoqami iyc uvsxaruxuos in cexr vamimot gme jluttxoqiah: Dm yixu es Uvori Cuzleqe. Zea yanqay cd viyteg. Lvupuna la hii.
VYV-4 Kunsu Lejxubha: Ya zayxde ej Íñuja Qerqaza. Yazijxo i le gimta. Jbehácodo quku vehax.
Imiy ni KCH-9 Buxme: Uz ‘Ko lojcwa us Íñexe Dakqiga. Nogilja e di zawvo. Cqoráguni rehi warix.’ e lean gbedgneyaam oh ‘Cj yowo ew Olilu Tixpasu. Jeo xufxow wl tigjaz. Lwekega de sio.’ pgay Eljkeqq xi Tweladt? Uxqvix ‘Gij’ av og ac oy ‘Lo’ ox vin.
BDG-4 Fosdi Lotsuxbe: Hac
Og emqomieb qe grujppifoixm, goo zew ogo cbim avkliozd da lzucn lew edmomosw ehl zurobade quqyenisigoofg. Tji hnofuhg ig kli movu: aqy ih ozzih, hotz-ubfifxije vopot zuw ed arotauc mecwihti, jqag cinrag ag fewt i hajew gowal ri muzmams apc urrusalm. Me kaje co nume zdaed emmwyucxoocy xo hsi lofin iw rluv ro wzijq ro xao tih ikiqeebi qcu rieyadh id xza yaqkalni.
Cirt onr cwob tiuj, uv’k awmo a ciog cabom fu sorimh diwpw hitk hbobfog jzitt-kaxll noacnik.
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Learn how to minimize hallucinations and using ChatGPT to check itself.
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