NEWSLETTER
Benchmarking custom models at Amazon Bedrock with Llmperf and Litellm
Open Foundation Models (FMS) allows organizations to build personalized ai applications by adjusting their specific domains or tasks, while retaining ...
Tag: benchmarking
Benchmarking Amazon Nova and GPT-4o models with FloTorch
Based on original post by Dr. Hemant Joshi, CTO, FloTorch.ai A recent evaluation conducted by <a target="_blank" href="https://www.flotorch.ai/" target="_blank" rel="noopener">FloTorch ...
Benchmarking OCR APIs on Real-World Documents
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DeepMind Research Introduces FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Inputs
Large language models (LLMs) have revolutionized natural language processing, enabling applications ranging from automated writing to complex decision aids. However, ...
Salesforce AI research proposes Programmatic VLM Evaluation (PROVE): a new benchmarking paradigm to evaluate VLM responses to open queries
Vision-language models (VLM) are increasingly used to generate answers to queries about visual content. Despite their advances, they often suffer ...
Quanda: A New Python Toolkit for Standardized Assessment and Benchmarking of Training Data Attribution (TDA) in Explainable AI
XAI, or explainable ai, represents a paradigm shift in neural networks that emphasizes the need to explain the decision-making processes ...
Windows Agent Arena (WAA) – A scalable, open-source Windows AI agent platform for testing and benchmarking multimodal desktop AI agents
artificial intelligence (ai) has made progress in developing agents capable of executing complex tasks on digital platforms. These agents, often ...
Benchmarking Hallucination Detection Methods in RAG | by Hui Wen Goh | Sep, 2024
Evaluating methods to enhance reliability in LLM-generated responses.Unchecked hallucination remains a big problem in today’s Retrieval-Augmented Generation applications. This study ...
Google AI introduces CardBench: a comprehensive benchmarking system that includes over 20 real-world databases and thousands of queries to revolutionize learned cardinality estimation
Cardinality estimation (CE) is crucial for optimizing query performance in relational databases. It involves predicting the number of intermediate results ...
Benchmarking large language models in biomedical classification and named entity recognition: Assessing the impact of stimulus techniques and domain knowledge
LLM models are increasingly used in healthcare for tasks such as question answering and document summarization, with performance similar to ...
