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,...
The case for evaluation of NLU platforms Synthetic image and video have proven to be a big success for cost-cutting. Synthetic text is following suit: tabular data (that is the data organized in a table with rows and columns) is becoming mainstream already, and the...
What Is Synthetic training data? Synthetic Training data is the data that is used to train an NLU engine. An NLU engine allows chatbots to understand the intent of user queries. The training data is enriched by data labeling or data annotation, with information about...
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