The integration of visual and textual data in artificial intelligence presents a complex challenge. Traditional models often struggle to interpret structured visual documents such as tables, charts, infographics, and diagrams with precision. This limitation affects automated content extraction and comprehension, which are crucial for applications in data analysis, information retrieval, and decision-making. As organizations increasingly…
After the success of large language models (LLMs), the current research extends beyond text-based understanding to multimodal reasoning tasks. These tasks integrate vision and language, which is essential for artificial general intelligence (AGI). Cognitive benchmarks such as PuzzleVQA and AlgoPuzzleVQA evaluate AI’s ability to process abstract visual information and algorithmic reasoning. Even with advancements, LLMs…
Reinforcement learning (RL) for large language models (LLMs) has traditionally relied on outcome-based rewards, which provide feedback only on the final output. This sparsity of reward makes it challenging to train models that need multi-step reasoning, like those employed in mathematical problem-solving and programming. Additionally, credit assignment becomes ambiguous, as the model does not get…
Aligning large language models (LLMs) with human values remains difficult due to unclear goals, weak training signals, and the complexity of human intent. Direct Alignment Algorithms (DAAs) offer a way to simplify this process by optimizing models directly without relying on reward modeling or reinforcement learning. These algorithms use different ranking methods, such as comparing…
LLM inference is highly resource-intensive, requiring substantial memory and computational power. To address this, various model parallelism strategies distribute workloads across multiple GPUs, reducing memory constraints and speeding up inference. Tensor parallelism (TP) is a widely used technique that partitions weights and activations across GPUs, enabling them to process a single request collaboratively. Unlike data…
Large Language Models (LLMs) such as GPT, Gemini, and Claude utilize vast training datasets and complex architectures to generate high-quality responses. However, optimizing their inference-time computation remains challenging, as increasing model size leads to higher computational costs. Researchers continue to explore strategies that maximize efficiency while maintaining or improving model performance. One widely adopted approach…
Databases are essential for storing and retrieving structured data supporting business intelligence, research, and enterprise applications. Querying databases typically requires SQL, which varies across systems and can be complex. While LLMs offer the potential for automating queries, most approaches rely on translating natural language to SQL, often leading to errors due to syntax differences. A…
Large language models (LLMs) have revolutionized artificial intelligence by demonstrating remarkable capabilities in text generation and problem-solving. However, a critical limitation persists in their default “fast thinking” approach—generating outputs based on a single query without iterative refinement. While recent “slow thinking” methods like chain-of-thought prompting break problems into smaller steps, they remain constrained by static…
In artificial intelligence and machine learning, high-quality datasets play a crucial role in developing accurate and reliable models. However, collecting extensive, verified data—particularly in specialized domains like mathematics, coding, and science—remains a challenge. Traditional data-gathering methods often fail to produce datasets that effectively train models for complex reasoning tasks. This gap highlights the need for…
Robots are usually unsuitable for altering different tasks and environments. General-purpose models of robots are devised to circumvent this problem. They allow fine-tuning these general-purpose models for a wide scope of robotic tasks. However, it is challenging to maintain the consistency of shared open resources across various platforms. Success in real-world environments is far from…