Future News, Today
Large language models (LLMs) have demonstrated impressive capabilities in in-context learning (ICL), a form of supervised learning that doesn’t require parameter updates. However, researchers are now exploring whether this ability…
Automatic benchmarks like AlpacaEval 2.0, Arena-Hard-Auto, and MTBench have gained popularity for evaluating LLMs due to their affordability and scalability compared to human evaluation. These benchmarks use LLM-based auto-annotators, which…
It’s hard to know where to focus when speaking to Christoph Kohstall. The contents of his packed Palo Alto garage compete for attention. To his immediate right stands a tower…
When you think of brain stimulating medtech, startups building wearables as therapeutics probably aren’t the first thing that springs to mind. Such tech is still flying fairly under the radar…
Back in 2021, Morgan Neville thought using AI to recreate the late Anthony Bourdain’s voice would be an interesting Easter egg in his documentary. He ended up being a canary…
High latency in time-to-first-token (TTFT) is a significant challenge for retrieval-augmented generation (RAG) systems. Existing RAG systems, which concatenate and process multiple retrieved document chunks to create responses, require substantial…
Large Language Models (LLMs) have gained significant attention for their versatility in various tasks, from natural language processing to complex reasoning. A promising application of these models is the development…
Scaling state-of-the-art models for real-world deployment often requires training different model sizes to adapt to various computing environments. However, training multiple versions independently is computationally expensive and leads to inefficiencies…
Ego-centric searches are essential in many applications, from financial fraud detection to social network research, because they concentrate on a single vertex and its immediate neighbors. These queries offer insights…
Retrieval-augmented generation (RAG) is a method that integrates external knowledge sources into large language models (LLMs) to provide accurate and contextually relevant responses. These systems enhance the ability of LLMs…