Introduction Reinforcement Learning from Human or AI Feedback (RLHF, RLAIF) has become the standard recipe for aligning large language models (LLMs). But as we push into the agentic era — where models call…
VIBEPASS, a new benchmark, reveals a fundamental weakness in modern AI coding assistants: even with near-perfect scores on code generation tasks, frontier models falter when it comes to finding and fixing subtle bugs…
AI agents that rely on web search are vulnerable to “well poisoning” attacks, where adversaries publish fabricated but authoritative-sounding content designed to be retrieved during search. Think “AI Slop” for agents. Our research…
What is FINDAP? The FINDAP Framework is a cutting-edge approach to fine-tune large language models (LLMs) specifically for the finance industry. While LLMs like ChatGPT or Bard are excellent general-purpose tools, specialized domains…
AI is rapidly transforming industries, helping businesses enhance customer experiences, improve efficiency, and make smarter decisions. But an essential question arises: How can we ensure that AI is creating accurate and grounded answers?…
The SFR-Embedding-Mistral marks a significant advancement in text-embedding models, building upon the solid foundations of E5-mistral-7b-instruct and Mistral-7B-v0.1.
As the development and deployment of large language models (LLMs) accelerates, evaluating model outputs has become increasingly important. The established method of evaluating responses typically involves recruiting and training human evaluators, having them…
Retrieval Augmented Generation (RAG) has not only gained steam as one of the most invested areas of research in generative AI but also gathered considerable popularity and commercialization opportunities. RAG is typically applied…
TL;DR: With CodeChain, a pretrained large language model (LLM) can solve challenging coding problems by integrating modularity in generation samples and self-improve by employing a chain of self-revisions on representative sub-modules. CodeChain can…
