Vision-Language Models (VLMs) struggle with spatial reasoning tasks like object localization, counting, and relational question-answering. This issue stems from Vision Transformers (ViTs) trained with image-level supervision, which often fail to encode localized information effectively, limiting spatial understanding. Researchers from Stanford University propose a novel solution called Locality Alignment, which involves a post-training stage for Vision…
Research on the robustness of LLMs to jailbreak attacks has mostly focused on chatbot applications, where users manipulate prompts to bypass safety measures. However, LLM agents, which utilize external tools and perform multi-step tasks, pose a greater misuse risk, especially in malicious contexts like ordering illegal materials. Studies show that defenses effective in single-turn interactions…
Large Language Model (LLM)–based online agents have significantly advanced in recent times, resulting in unique designs and new benchmarks that show notable improvements in autonomous web navigation and interaction. These advancements demonstrate how web agents can increasingly carry out intricate online tasks more accurately and effectively. However, many of the current benchmarks overlook important factors…
The newly launched ChatGPT Windows app (beta version) by OpenAI aims to address several challenges and create a more streamlined user experience for individuals and businesses alike. One of the most significant issues it seeks to solve is the need for quick, seamless access to AI assistance without relying on a web browser. Users have…
Jina AI announced the release of their latest product, g.jina.ai, designed to tackle the growing problem of misinformation and hallucination in generative AI models. This innovative tool is part of their larger suite of applications to improve factual accuracy and grounding in AI-generated and human-written content. Focusing on Large Language Models (LLMs), g.jina.ai integrates real-time…
The PyTorch community has continuously been at the forefront of advancing machine learning frameworks to meet the growing needs of researchers, data scientists, and AI engineers worldwide. With the latest PyTorch 2.5 release, the team aims to address several challenges faced by the ML community, focusing primarily on improving computational efficiency, reducing start up times,…
One of the biggest hurdles organizations face is implementing Large Language Models (LLMs) to handle intricate workflows effectively. Issues of speed, flexibility, and scalability often hinder the automation of complex workflows requiring coordination across multiple systems. Enterprises struggle with the cumbersome nature of configuring LLMs for seamless collaboration across data sources, making it challenging to…
Large Language Models (LLMs) have demonstrated remarkable proficiency in In-Context Learning (ICL), which is a technique that teaches them to complete tasks using just a few examples included in the input prompt and no further training. One of the primary features of ICL is that these models can manage several computationally different ICL tasks simultaneously…
There is a growing demand for embedding models that balance accuracy, efficiency, and versatility. Existing models often struggle to achieve this balance, especially in scenarios ranging from low-resource applications to large-scale deployments. The need for more efficient, high-quality embeddings has driven the development of new solutions to meet these evolving requirements. Overview of Sentence Transformers…
There is a need for flexible and efficient adaptation of large language models (LLMs) to various tasks. Existing approaches, such as mixture-of-experts (MoE) and model arithmetic, struggle with requiring substantial tuning data, inflexible model composition, or strong assumptions about how models should be used. These limitations call for a methodology that can adapt LLMs efficiently…