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,…
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…
Medical image segmentation, the identification, and outlining of anatomical structures within medical scans, plays a crucial role in the accurate diagnosis, treatment planning, and monitoring of diseases. Recent advances in deep learning models such as U-NET, extensions of U-NET, and the Segment Anything Model (SAM) have significantly improved the accuracy and efficiency of medical image…
Artificial Intelligence (AI) safety continues to become an increasing concern as AI systems become more powerful. This has led to AI safety research aiming to address the imminent and future risks through the development of benchmarks to measure safety properties such as fairness, reliability, and robustness. However, these benchmarks are not always clear in defining…
As an area of Artificial Intelligence (AI), Reinforcement Learning (RL) enables agents to learn by interacting with their environment and making decisions that maximize their cumulative rewards over time. This learning approach is especially useful in robotics and autonomous systems due to its focus on trial and error learning. However, RL faces challenges in situations…
Representational similarity measures are essential instruments in machine learning as they facilitate the comparison of internal representations of neural networks, aiding researchers in understanding how various neural network layers and architectures process information. These measures are vital for understanding the performance, behavior, and learning dynamics of a model. However, the development and application of these…
Advancements in Large Language Models (LLMs) have notably benefitted the development of artificial intelligence, particularly in creating agent-based systems. These systems are designed to interact with various environments and carry out actions to meet specific goals. One of the significant challenges includes the creation of elaborate planning environments and tasks, most of which currently rely…
Kolmogorov-Arnold Networks (KANs) are a recent development that offer an alternative to Multi-Layer Perceptrons (MLPs) in machine learning. Using the Kolmogorov-Arnold representation theorem, KANs use neurons that carry out simple addition operations. Nonetheless, current models of KANs can pose challenges in real-world application, prompting researchers to explore other multivariate functions that could boost its use…
Multi-layer perceptrons (MLPs) are integral to modern deep learning models for their versatility in replicating nonlinear functions across various tasks. However, interpretation and scalability challenges and reliance on fixed activation functions have raised concerns about their adaptability and scalability. Researchers have explored alternative architectures to overcome these issues, such as Kolmogov-Arnold Networks (KANs).
KANs have…