Getting GPT to Answer Consistently and with Style GPT, like other generative models, tends to provide disparate answers for the same question. Sometimes answers vary only slightly, but sometimes they are very inconsistent or even contradictory. Behavior of Standard...
Technological Alchemy: Using GPT in CX According to Blake Morgan, a CX expert, ChatGPT has major flaws that prevent it from becoming a useful tool in industries like Customer Experience. In, Cons Of ChatGPT For Customer Experience, an article recently published in...
The advent of Generative Large Language Models (LLMs) has revolutionized a myriad of business operations, ranging from creating enthralling blog articles to providing tailored customer support. These models, especially when spiced with focused datasets, open a...
In a previous post, we conducted a comprehensive benchmark on the role of synthetic text generation for intent detection using traditional Natural Language Understanding (NLU) platforms. In that study, we specifically examined the performance of Rasa as an example,...
We have shown in previous posts why Synthetic Training Data is the best way to boost the accuracy of any chatbot, and the solution to the most important problem of chatbots nowadays: data scarcity, namely, the lack of accurate and useful training data for the problems...
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