Azure OpenAI (opens in new tab) (AOAI) provides solutions to evaluate your LLM-based features and apps on multiple dimensions of quality, safety, and performance. Teams leverage those evaluation methods before, during and after deployment to minimize negative user experience and manage ...
Coping with model hallucinations during evaluation: hallucinations, where the LLM generates textually coherent but factually incorrect information, are hard to spot and evaluate. Investigate and use highly specialized metrics like FEVER, that assess factual accuracy, or rely on human reviewers to detect...
evaluation of the capabilities and cognitive abilities of those new models have become much closer in essence to the task of evaluating those of a human rather than those of a narrow AI model” [1].Measuring LLM performance on user traffic in real product scenarios...
We used the following metrics to evaluate embedding performance: Embedding latency: Time taken to create embeddings Retrieval quality: Relevance of retrieved documents to the user query Hardware used 1 NVIDIA T4 GPU, 16GB Memory Where’s the code? Evaluation notebooks for each of the above embedding...
How to evaluate a RAG application Before we begin, it is important to distinguish LLM model evaluation from LLM application evaluation. Evaluating LLM models involves measuring the performance of a given model across different tasks, whereas LLM application evaluation is about evaluating different compone...
Analyzes trends and patterns to forecast project timelines, resource needs, and potential risks Predicts resource availability and workload, helping teams allocate resources efficiently Simulates different project scenarios, helping teams evaluate potential outcomes and choose the best course of action ...
1.Model size vs. performance Large models: LLMs are well-known for their impressive performance across a range of tasks, thanks to their massive number of parameters. For example, GPT-3 boasts 175 billion parameters, while PaLM scales up to 540 billion parameters. This enormous size allows LL...
These phases have quite different performance profiles. The Prefill Phase requires just one invocation of the LM, requiring the fetch of all the parameters of the model once from the DRAM, and reuses it m times to process all the m tokens in the prompt. With sufficiently ...
To evaluate QA models, we use special collections of questions and answers, like SQuAD (Stanford Question Answering Dataset), Natural Questions, or TriviaQA. Each one is like a different game with its own rules. For example, SQuAD is about finding answers in a given text, while others are ...
To successfully fine tune LLM and evaluate it, especially those used in NLP services, the following best practices should be considered: Comprehensive Evaluation Framework Establish a structured evaluation framework before deployment, covering performance metrics, scalability, bias detection, and robustness ...