All tags
Topic: "dataset-quality"
Microsoft AgentInstruct + Orca 3
mistral-7b orca-2.5 microsoft-research apple tencent hugging-face synthetic-data fine-tuning instruction-following transformers model-performance hallucination-detection dataset-quality flashattention mixture-of-experts philschmid sama bindureddy rohanpaul_ai zachtratar dair_ai
Microsoft Research released AgentInstruct, the third paper in its Orca series, introducing a generative teaching pipeline that produces 25.8 million synthetic instructions to fine-tune mistral-7b, achieving significant performance gains: +40% AGIEval, +19% MMLU, +54% GSM8K, +38% BBH, +45% AlpacaEval, and a 31.34% reduction in hallucinations. This synthetic data approach follows the success of FineWeb and Apple's Rephrasing research in improving dataset quality. Additionally, Tencent claims to have generated 1 billion diverse personas for synthetic data. On AI Twitter, notable discussions included a shooting incident at a Trump rally and recent ML research highlights such as FlashAttention-3, RankRAG, and Mixture of A Million Experts.
Qdrant's BM42: "Please don't trust us"
claude-3.5-sonnet gemma-2 nano-llava-1.5 qdrant cohere stripe anthropic hugging-face stablequan_ai semantic-search benchmarking dataset-quality model-evaluation model-optimization vision fine-tuning context-windows nils-reimers jeremyphoward hamelhusain rohanpaul_ai
Qdrant attempted to replace BM25 and SPLADE with a new method called "BM42" combining transformer attention and collection-wide statistics for semantic and keyword search, but their evaluation using the Quora dataset was flawed. Nils Reimers from Cohere reran BM42 on better datasets and found it underperformed. Qdrant acknowledged the errors but still ran a suboptimal BM25 implementation. This highlights the importance of dataset choice and evaluation sanity checks in search model claims. Additionally, Stripe faced criticism for AI/ML model failures causing account and payment issues, prompting calls for alternatives. Anthropic revealed that Claude 3.5 Sonnet suppresses some answer parts with backend tags, sparking debate. Gemma 2 model optimizations allow 2x faster fine-tuning with 63% less memory and longer context windows, running up to 34B parameters on consumer GPUs. nanoLLaVA-1.5 was announced as a compact 1B parameter vision model with significant improvements.