Recently, diffusion models have become powerful tools in various fields, like image and 3D object generation. Their success comes from their ability to handle denoising tasks with different types of noise, efficiently turning random noise into the target data distribution through repeated denoising steps. Using Transformer-based structures, it has been shown that adding more parameters…
Large language models (LLMs) demonstrate proficiency in information retrieval and creative writing, with notable improvements in mathematics and coding. ZebraLogic, a benchmark consisting of Logic Grid Puzzles, assesses LLMs’ logical reasoning capabilities. Each puzzle presents N houses with M features, requiring unique value assignments based on given clues. This task, a Constraint Satisfaction Problem (CSP),…
DeepSeek has recently released its latest open-source model on Hugging Facel, DeepSeek-V2-Chat-0628. This release marks a significant advancement in AI-driven text generation and chatbot technology capabilities, positioning DeepSeek at the forefront of the industry. DeepSeek-V2-Chat-0628 is an enhanced iteration of the previous DeepSeek-V2-Chat model. This new version has been meticulously refined to deliver superior performance…
Automating mathematical reasoning has long been a goal in artificial intelligence, with formal frameworks like Lean 4, Isabelle, and Coq playing a significant role. These frameworks enable users to write machine-verifiable proofs of mathematical theorems, providing a structured environment for proving complex problems. Developing neural theorem-provers, which aim to automate this process, requires rigorous benchmarks…
Language models (LMs) face significant challenges related to privacy and copyright concerns due to their training on vast amounts of text data. The inadvertent inclusion of private and copyrighted content in training datasets has led to legal and ethical issues, including copyright lawsuits and compliance requirements with regulations like GDPR. Data owners increasingly demand the…
As LLMs become increasingly integral to various AI tasks, their massive parameter sizes lead to high memory requirements and bandwidth consumption. While quantization-aware training (QAT) offers a potential solution by allowing models to operate with lower-bit representations, existing methods often require extensive training resources, making them impractical for large models. The research paper addresses the…
Evaluating large language models (LLMs) has become increasingly challenging due to their complexity and versatility. Ensuring the reliability and quality of these models’ outputs is crucial for advancing AI technologies and applications. Researchers need help developing reliable evaluation methods to assess the accuracy and impartiality of LLMs’ outputs, given human evaluations’ subjective, inconsistent, and costly…
High-dimensional clinical data (HDCD) refers to datasets in healthcare where the number of variables (or features) is significantly larger than the number of patients (or observations). As the number of variables increases, the data space grows exponentially, requiring substantial computational resources that make it difficult to process and analyze. Additionally, models built on high-dimensional data…
Large language models (LLMs) have showcased remarkable capabilities in generating content and solving complex problems across various domains. However, a notable challenge persists in their ability to perform multi-step deductive reasoning. This type of reasoning requires a coherent and logical thought process over extended interactions, which current LLMs need help with due to their training…
Using offline web apps and AI apps often comes with challenges. Users typically need to navigate multiple steps to get an app running. These steps can be confusing and time-consuming, especially for those who are not tech-savvy. Additionally, managing and customizing these apps often requires manual editing of files, making the process even more cumbersome.…