Prompts

Aligning Vision and Language: Driving Consistency in Unified Models with CocoCon

Unified vision language models have emerged as a frontier, combining the visual with the verbal to create models that can interpret images and respond to human language. However, one obstacle to their development has been ensuring that these models behave consistently...

CMU Researchers Introduce Sequoia: A Scalable, Robust, and Informed Algorithm for Speculative Decoding

Effective support of LLMs becomes more critical as large language models (LLMs) become widely used. Since obtaining a new token involves obtaining all parameters of LLM, speeding up LLM inference is difficult. The hardware is not used throughout production due to this...

Revolutionary 3D scene modeling with generalized exponential bounce

In 3D reconstruction and production, the pursuit of techniques that balance visual richness with computational efficiency is paramount. Effective methods such as Gaussian Splatting often have significant limitations, particularly in handling high-frequency and...

This machine learning research from Amazon presents BASE TTS: A Text-to-Speech (TTS) Model Representing Big Adaptive Streamable TTS with Emergent Abilities

Recent advances in generative deep learning models have revolutionized fields such as Natural Language Processing (NLP) and Computer Vision (CV). Previously, specialized models with supervised training dominated these areas, but now, a shift towards generalized models...

University of Washington researchers present Fiddler: A Resource-Efficient Inference Engine for LLMs with CPU-GPU Orchestration

Mixture of Experts (MoE) models have revolutionized artificial intelligence by enabling the dynamic allocation of tasks to specialized components in larger models. However, a major challenge for the adoption of MoE models is their deployment in environments with...

This machine learning study tests the transformer’s length generalization ability using the task of adding two integers

Transformer-based models have transformed the fields of Natural Language Processing (NLP) and Natural Language Generation (NLG), demonstrating excellent performance in a wide range of applications. The best examples of these are the recently introduced Gemini models...

Google DeepMind introduces round-trip correctness for evaluating large language models

The emergence of code-generating Large Language Models (LLMs) has marked a major leap forward. Able to understand and generate code, these models are revolutionizing the way developers approach coding tasks. From automating mundane tasks to fixing complex bugs, LLMs...

Scaling Up LLM Agents: Unlocking Enhanced Performance Through Simplicity

While large language models (LLMs) excel in many areas, they can handle complex tasks that require precise reasoning. Recent solutions often focus on sophisticated ensemble methods or frameworks where multiple LLM agents work together. These approaches certainly...

ByteDance Introduces Magic-Me: A New AI Framework for Creating Videos with Custom Identity

Text-to-Image (T2I) and Text-to-Video (T2V) generation has made significant strides in production models. While T2I models can control subject identity well, extending this capability to T2V remains a challenge. Existing T2V methods need more precise control of the...

Technion Researchers Revolutionize Audio Processing: Unleashing Creativity with Zero-Shot Techniques and Pretrained Models

Advances in creative media creation, with audio processing at the forefront of this technological renaissance. The innovative use of Large Language Models (LLM) for content generation and processing is now being explored in the audio landscape. Researchers from the...

Stanford researchers propose a family of representation adaptation (ReFT) methods that operate on a frozen base model and learn task-specific interventions on hidden representations

Apr 16, 2024 | Prompts

Pretrained language models (LMs) are usually fine-tuned to adapt them to new domains or tasks, a process known as micro-tuning. While refinement allows adaptation to various functions with small amounts of in-domain data, it can be prohibitively expensive for large...

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