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Researchers from Tsinghua University Introduce LLM4VG: A Novel AI Benchmark for Evaluating LLMs on Video Grounding Tasks

Large Language Models (LLMs) have recently extended their reach beyond traditional natural language processing, demonstrating significant potential in tasks requiring multimodal information. Their integration with video perception abilities is particularly noteworthy, a pivotal move in artificial intelligence. This research takes a giant leap in exploring LLMs’ capabilities in video grounding (VG), a critical task in…

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Researchers from UCSD and NYU Introduced the SEAL MLLM framework: Featuring the LLM-Guided Visual Search Algorithm V ∗ for Accurate Visual Grounding in High-Resolution Images

The focus has shifted towards multimodal Large Language Models (MLLMs), particularly in enhancing their processing and integrating multi-sensory data in the evolution of AI. This advancement is crucial in mimicking human-like cognitive abilities for complex real-world interactions, especially when dealing with rich visual inputs. A key challenge in the current MLLMs is their need for…

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LLMs Are Dumber Than a House Cat. Can they replace you anyway? | by Nabil Alouani | Jan, 2024

Not to pick on Sebastian Bubeck in particular, but if auto-complete-on-steroid can “blow his mind,” imagine the effects on the average user. Developers and data practitioners use LLMs every day to generate code, synthetic data, and documentation. They too can be misled by inflated capabilities. It’s when humans over-trust their tools that mistakes happen. TL;DR:…

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