Why Do You Need to Fine-tune Your Conversational LLM with 100’s (If Not 1,000’s) of Examples?
For Consistent LLM Answers, Fine-tune with Examples. LOTS of Examples LLMs tend to be very creative and introduce diversity and creativity in answers. That’s good for certain types of questions like: What can you tell me about La Cibeles? What gothic buildings should...
Introducing a New Breed of Data to Fine-tune LLMs: Hybrid Datasets
Download the dataset on our Github or Huggingface profile Synthetic Data and Expert Curation. In the dynamic world of AI and chatbot technology, the right dataset can make the difference between a run-of-the-mill virtual assistant and a truly engaging conversational...
GPT Referee: Using GPT-4 to Evaluate Synthetically Generated Responses in Conversational Systems
Introduction: At Bitext, we value data-driven analysis. Therefore, we’ve thoroughly assessed our Hybrid Datasets using our top-notch AI text generator. We initiated this assessment using GPT-4, which is well-regarded for evaluating language model responses. We...
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