Haize Labs has developed Sphynx, a groundbreaking tool designed to combat the issue of "hallucination" in AI models. In AI, hallucination refers to the scenario where a language model produces incorrect or nonsensical outputs, despite its capabilities, posing a significant problem for numerous AI applications and demanding improved detection methods.
Hallucinations hinder the effectiveness of large…
The BRAG series is a set of high-performance Retrieval Augmented Generation (RAG) models developed by Maximalists AI Researcher. They are a small language model designed to be a low-cost alternative for AI-driven language processing, proving effective in artificial intelligence due to their affordability and cost-effectiveness. They were created to meet the need for more powerful…
Artificial Intelligence (AI) is transforming a multitude of industries at an exponential rate. In particular, AI agents designed to streamline and automate various aspects of business operations are emerging as some of the most innovative recent developments. These agents broadly fall into three categories: Planning Agents, Workflow Agents, and Matrix Agents. Each type of agent…
Improving Text Embeddings in Compact Language Models: A Comparative Refinement Method using MiniCPM.
Researchers from Tsinghua University have developed an approach to improve the performance of smaller language models such as MiniCPM, Phi-2, and Gemma by enhancing their text embeddings. By applying contrastive fine-tuning using the NLI dataset, the researchers significantly improved the text embedding quality across various benchmarks. In particular, MiniCPM showed a significant 56.33% performance improvement,…
Despite their crucial function in our digital lives, many search engines still struggle to deliver relevant and accurate results, leading to user frustration. Often, these issues stem from limitations in the underlying technologies used by these search engines. Several have tried to address these problems by integrating advanced algorithms and machine learning models into their…
OWLSAM2 is an innovative project that combines the strengths of OWLv2 and SAM2, two advanced models in the field of computer vision, to create a text-promptable model for zero-shot object detection and mask generation. OWLv2 stands out for its zero-shot object detection abilities that enable it to identify objects based on textual descriptions alone, without…
Introducing OWLSAM2: An unparalleled project that merges the sophisticated zero-shot object recognition attributes of OWLv2, renowned for its ability to identify objects in images without needing specific dataset training, and the highly advanced mask generation proficiencies of SAM2 (Segment Anything Model 2). This novel integration consequently leads to the creation of a text-prompted model that…
Large Language Models (LLMs) have drastically changed machine learning, pushing the field from traditional end-to-end training towards the use of pretrained models with carefully crafted prompts. This move has created a compelling question for researchers: Can a pretrained LLM function similar to a neural network, parameterized by its natural language prompt?
LLMs have been used for…
Researchers from the University of Toronto and the Vector Institute have developed an advanced framework for protein language models (PLMs), called Protein Annotation-Improved Representations (PAIR). This framework enhances the ability of models to predict amino acid sequences and generate feature vectors representing proteins, proving particularly useful in predicting protein folding and mutation effects.
PLMs traditionally make…