With the current conversation about widespread LLMs in AI, it is crucial to understand some of the basics involved. Despite their general-purpose pretraining in developing LLMs, most require fine-tuning to excel in specific tasks, domains, or applications. Fine-tuning tailors a model’s performance, making it efficient and precise for specialized use cases. Today, let’s examine the…
GUI agents seek to perform real tasks in digital environments by understanding and interacting with graphical interfaces such as buttons and text boxes. The biggest open challenges lie in enabling agents to process complex, evolving interfaces, plan effective actions, and execute precision tasks that include finding clickable areas or filling text boxes. These agents also…
Retrieval-Augmented Generation (RAG) is a key technique in enterprise applications that combines large foundation models with external retrieval systems to generate responses that are both accurate and grounded in factual information. Unlike traditional foundation models, which are trained on massive datasets and remain static post-deployment, RAG enhances reliability by incorporating real-time or domain-specific information during…
Researchers have highlighted concerns regarding hallucinations in LLMs due to their generation of plausible but inaccurate or unrelated content. However, these hallucinations hold potential in creativity-driven fields like drug discovery, where innovation is essential. LLMs have been widely applied in scientific domains, such as materials science, biology, and chemistry, aiding tasks like molecular description and…
Large Language Models (LLMs) have become an indispensable part of contemporary life, shaping the future of nearly every conceivable domain. They are widely acknowledged for their impressive performance across tasks of varying complexity. However, instances have arisen where LLMs have been criticized for generating unexpected and unsafe responses. Consequently, ongoing research aims to align LLMs…
Knowledge distillation, a crucial technique in artificial intelligence for transferring knowledge from large language models (LLMs) to smaller, resource-efficient ones, faces several significant challenges that limit its utility. Over-distillation tends to cause homogenization, in which student models over-imitate teacher models and lose diversity and the capacity to solve novel or challenging tasks. Also, the non-transparent…
Multimodal AI integrates diverse data formats, such as text and images, to create systems capable of accurately understanding and generating content. By bridging textual and visual data, these models address real-world problems like visual question answering, instruction-following, and creative content generation. They rely on advanced architectures and large-scale datasets to enhance performance, focusing on overcoming…
SSL is a powerful technique for extracting meaningful patterns from large, unlabelled datasets, proving transformative in fields like computer vision and NLP. In single-cell genomics (SCG), SSL offers significant potential for analyzing complex biological data, especially with the advent of foundation models. SCG, fueled by advances in single-cell RNA sequencing, has evolved into a data-intensive…