Optimize your large language model's potential for better output generation. Explore techniques, fine-tuning, and responsible use in this comprehensive guide.
As LLMs get used at large scale, it is critical to measure and detect anyResponsible AI(opens in new tab)issues that arise.Azure OpenAI(opens in new tab)(AOAI) provides solutions to evaluate your LLM-based features and apps on multiple dimensions of quality, sa...
Using RAG with an LLM has been shown toreduce hallucinationsand improve accuracy. However, using RAG also adds a new component that requires testing its relevancy and performance. The types of testing depend on how easy it is to evaluate the RAG and LLM’s responses and to what extent develo...
However, using RAG also adds a new component that requires testing its relevancy and performance. The types of testing depend on how easy it is to evaluate the RAG and LLM’s responses and to what extent development teams can leverage end-user feedback. I recently spoke with Deon Nicholas,...
You can grab those models with one line of code and evaluate them, test them, and customize them. The models are pretrained and ready to go, so you can experiment with them in a matter of hours—not days, weeks, or months. Arun Gupta: Can LLMs only come from corporation...
Regarding the SFT strategies, we evaluate sequential learning multiple abilities are prone to catastrophic forgetting. Our proposed Dual-stage Mixed Fine-tuning (DMT) strategy learns specialized abilities first and then learns general abilities with a small amount of specialized data to prevent forgetting...
2. Accuracy is used to evaluate the composition information. For each sample, we computed the accuracy by dividing the number of correct key-value pairs by the total number of key-value fields being checked. Then, we averaged these accuracies across all samples to find the accuracy of the ...
, following a linear warmup and cosinedecay schedule. We conduct all experiments onthe Pythia 410M language model architecture andevaluate performance through validation perplex-ity. We experiment with different pre-trainingcheckpoints, variousmaximumlearningrates, andvarious warmup lengths. Our results ...
Self-rewarding language models (SRLM) create their own training examples and evaluate them (source: arxiv) Self-rewarding language models start with a foundational LLM trained on a large corpus of text. The model is then fine-tuned on a small seed of human-annotated examples. The seed data...
To evaluate Orca 2, we use a comprehensive set of 15 diverse benchmarks that correspond to approximately 100 tasks and more than 36,000 unique test cases in zero-shot settings. The benchmarks cover a variety of aspects, including language understanding, common-sense reasoning, multi-step reaso...