🚀 A game-changer benchmark: LLM-Uncertainty-Bench 🌟 📚 We introduce "Benchmarking LLMs via Uncertainty Quantification", which challenges the status quo in LLM evaluation. 💡 Uncertainty matters too: we propose a novel uncertainty-aware metric, which tests 8 LLMs across 5

FuseChat Knowledge Fusion of Chat Models While training large language models (LLMs) from scratch can indeed lead to models with distinct capabilities and strengths, this approach incurs substantial costs and may lead to potential redundancy in competencies. An alternative

Open-sourced Multimodal models -- fascinating Open-sourced MOE models -- fascinating Open-sourced Multimodal MOE models -- WOW! check this out 👇

🚀Check out VideoVista, a comprehensive video-LMMs evaluation benchmark! videovista.github.io 🚀 Dive into our leaderboard: - 📊 Evaluating 33 Video-LMMs across 27 tasks; - 🥉 The latest GPT-4o-Mini clinches 3rd place; - 🏆 InternLM-XComposer-2.5 emerges as the

To Code, or Not To Code? Exploring Impact of Code in Pre-training discuss: huggingface.co/papers/2408.10… Including code in the pre-training data mixture, even for models not specifically designed for code, has become a common practice in LLMs pre-training. While there has been

Exciting to see our old friend continuing to push the real-world boundaries of LLM applications (shoutout to MT here)!

These findings resonate with my impressions. AFAIC, structured prompting outperforms CoT & ICL by steering LLMs through workflows. Great to see this ‘rebuttal’ backed by such rigorous analysis — reminds me of the insights in LLMs Cannot Self-Correct. We need more like this!

🌺GPT-4o’s image generation is stunning — but how well does it handle complex scenarios? 🤔 We introduce 🚀CIGEVAL🚀, a novel method to evaluate models' capabilities in Conditional Image Generation 🖼️➕🖼️🟰🖼️. Find out how top models perform when conditions get truly