Large Language Models (LLMs) aim to align with human preferences, ensuring reliable and trustworthy decision-making. However, these models acquire biases, logical leaps, and hallucinations, rendering them invalid and harmless for critical tasks involving logical thinking. Logical consistency problems make it impossible to develop logically consistent LLMs. They also use temporal reasoning, optimization, and automated systems,…
Owing to the advent of Artificial Intelligence (AI), the software industry has been leveraging Large Language Models (LLMs) for code completion, debugging, and generating test cases. However, LLMs follow a generic approach when developing test cases for a different software, which prevents them from considering the software’s unique architecture, user requirements and potential edge cases.…
Large Language Models (LLMs) have significantly advanced artificial intelligence, particularly in natural language understanding and generation. However, these models encounter difficulties with complex reasoning tasks, especially those requiring multi-step, non-linear processes. While traditional Chain-of-Thought (CoT) approaches, which promote step-by-step reasoning, improve performance on simpler tasks, they often fall short in addressing more intricate problems. This…
Artificial intelligence research has steadily advanced toward creating systems capable of complex reasoning. Multimodal large language models (MLLMs) represent a significant development in this journey, combining the ability to process text and visual data. These systems can address intricate challenges like mathematical problems or reasoning through diagrams. By enabling AI to bridge the gap between…
The generation of synthetic tabular data has become increasingly crucial in fields like healthcare and financial services, where privacy concerns often restrict the use of real-world data. While autoregressive transformers, masked transformers, and diffusion models with transformers, have shown significant success in generating high-quality synthetic data with strong fidelity, utility, and privacy guarantees, they face…
Microsoft has open-sourced Phi-4, a compact and efficient small language model, on Hugging Face under the MIT license. This decision highlights a shift towards transparency and collaboration in the AI community, offering developers and researchers new opportunities. What Is Microsoft Phi-4? Phi-4 is a 14-billion-parameter language model developed with a focus on data quality and…
Language-based agentic systems represent a breakthrough in artificial intelligence, allowing for the automation of tasks such as question-answering, programming, and advanced problem-solving. These systems, heavily reliant on Large Language Models (LLMs), communicate using natural language. This innovative design reduces the engineering complexity of individual components and enables seamless interaction between them, paving the way for…
The o1 model’s impressive performance in complex reasoning highlights the potential of test-time computing scaling, which enhances System-2 thinking by allocating greater computational effort during inference. While deep learning’s scaling effects have driven advancements in AI, particularly in LLMs like GPT, further scaling during training faces limitations due to data scarcity and computational constraints. Additionally,…