Superalignment Fast Grants
We’re launching $10M in grants to support technical research towards the alignment and safety of superhuman AI systems, including weak-to-strong generalization, interpretability, scalable oversight, and more.
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ByteDance AI Research Unveils Reinforced Fine-Tuning (ReFT) Method to Enhance the Generalizability of Learning LLMs for Reasoning with Math Problem Solving as an Example
One effective method to improve the reasoning skills of LLMs is to employ supervised fine-tuning (SFT) with chain-of-thought (CoT) annotations. However, this approach has limitations in terms of generalization because it heavily depends on the provided CoT data. In scenarios like math problem-solving, each question in the training data typically has only one annotated reasoning…
This AI Paper from Google Unveils the Intricacies of Self-Correction in Language Models: Exploring Logical Errors and the Efficacy of Backtracking
Large Language Models are being used in various fields. With the growth of AI, the use of LLMs has further increased. They are used in various applications together with those that require reasoning, such as answering multiple-turn questions, completing tasks, and generating code. However, these models are not completely reliable as they may provide inaccurate…
AI News Weekly – Issue #376: Unlock AI: Top Tips for Election Officials – Mar 14th 2024
Powered by global.ntt Welcome Samsung AI Forum 2023 underway to showcase artificial intelligence advancements Called the Samsung AI Forum, the event is now underway in Seoul, South Korea. Safety and trustworthiness is at the top of mind for researchers Samsung AI Forum will host over 1,000 attendees, including researchers, academics, industry experts, and students. The…
Meet BiTA: An Innovative AI Method Expediting LLMs via Streamlined Semi-Autoregressive Generation and Draft Verification
Large language models (LLMs) based on transformer architectures have emerged in recent years. Models such as Chat-GPT and LLaMA-2 demonstrate how the parameters of LLMs have rapidly increased, ranging from several billion to tens of trillions. Although LLMs are very good generators, they have trouble with inference delay since there is a lot of computing…
Google DeepMind Researchers Propose a Novel AI Method Called Sparse Fine-grained Contrastive Alignment (SPARC) for Fine-Grained Vision-Language Pretraining
Contrastive pre-training using large, noisy image-text datasets has become popular for building general vision representations. These models align global image and text features in a shared space through similar and dissimilar pairs, excelling in tasks like image classification and retrieval. However, they need help with fine-grained tasks such as localization and spatial relationships. Recent efforts…
Meet PIXART-δ: The Next-Generation AI Framework in Text-to-Image Synthesis with Unparalleled Speed and Quality
In the landscape of text-to-image models, the demand for high-quality visuals has surged. However, these models often need to grapple with resource-intensive training and slow inference, hindering their real-time applicability. In response, this paper introduces PIXART-δ, an advanced iteration that seamlessly integrates Latent Consistency Models (LCM) and a custom ControlNet module into the existing PIXART-α…
