​[[{“value”:” Instruction-tuned LLMs can handle various tasks using natural language instructions, but their performance is sensitive to how instructions are phrased. This issue is critical in healthcare, where clinicians, who may need to be more skilled, prompt engineers, need reliable outputs. The robustness of LLMs… Read More »Evaluating the Robustness and Fairness of Instruction-Tuned LLMs in Clinical Tasks: Implications for Performance Variability and Demographic Fairness Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Traditional methods, relying solely on formal proof data, overlook valuable informal reasoning processes crucial to human mathematicians. The absence of natural language thought processes in formal proofs creates a significant gap between human reasoning and machine-driven proofs. Existing language models specialized for generating tactics… Read More »How can Informal Reasoning Improve Formal Theorem Proving? This AI Paper Introduces an AI Framework for Learning to Interleave Informal Thoughts with Steps of Formal Proving Shoaib Nazir Artificial Intelligence Category – MarkTechPost

​[[{“value”:” 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.… Read More »DiT-MoE: A New Version of the DiT Architecture for Image Generation Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” 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… Read More »ZebraLogic: A Logical Reasoning AI Benchmark Designed for Evaluating LLMs with Logic Puzzles Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” 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.… Read More »DeepSeek-V2-0628 Released: An Improved Open-Source Version of DeepSeek-V2 Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” 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… Read More »UT Austin Researchers Introduce PUTNAMBENCH: A Comprehensive AI Benchmark for Evaluating the Capabilities of Neural Theorem-Provers with Putnam Mathematical Problems Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” 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… Read More »MUSE: A Comprehensive AI Framework for Evaluating Machine Unlearning in Language Models Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” 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,… Read More »Efficient Quantization-Aware Training (EfficientQAT): A Novel Machine Learning Quantization Technique for Compressing LLMs Shreya Maji Artificial Intelligence Category – MarkTechPost

​[[{“value”:” 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… Read More »This AI Paper from Google AI Introduces FLAMe: A Foundational Large Autorater Model for Reliable and Efficient LLM Evaluation Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” 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… Read More »Google Research Presents a Novel AI Method for Genetic Discovery that can Harness Hidden Information in High-Dimensional Clinical Data Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost