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Posts tagged Large Language Models
A Metrics-First Approach to LLM Evaluation
Learn about different types of LLM evaluation metrics needed for generative applications
Deep Dive Into LLM Hallucinations Across Generative Tasks
The creation of human-like text with Natural Language Generation (NLG) has improved recently because of advancements in Transformer-based language models. This has made the text produced by NLG helpful for creating summaries, generating dialogue, or transforming data into text. However, there is a problem: these deep learning systems sometimes make up or "hallucinate" text that was not intended, which can lead to worse performance and disappoint users in real-world situations.
Pinecone + Galileo = get the right context for your prompts
Galileo LLM Studio enables Pineonce users to identify and visualize the right context to add powered by evaluation metrics such as the hallucination score, so you can power your LLM apps with the right context while engineering your prompts, or for your LLMs in production
5 Techniques for Detecting LLM Hallucinations
A survey of hallucination detection techniques
Announcing LLM Studio: A Smarter Way to Build LLM Applications
LLM Studio helps you develop and evaluate LLM apps in hours instead of days.
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Natural Language Processing
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