GPT-4 IS MORE ACCURATE WHEN ASKED TO CRITIQUE ITSELF
Researchers have found that asking GPT-4 to critique its own work leads to a 30% performance boost.
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Researchers have found that asking GPT-4 to critique its own work leads to a 30% performance boost.
GPTEVAL introduces a groundbreaking framework that leverages large language models with chain-of-thoughts and form-filling paradigms to assess the quality of natural language generation outputs. Demonstrating a remarkable improvement in correlation with human judgments, this novel approach also uncovers potential biases in LLM-based evaluators.
∞-Diff is introduced as a novel generative diffusion model that operates on infinite resolution data, learning a continuous function for sampling at any resolution. This approach surpasses other infinite resolution models by working directly on raw data, yielding higher sample quality and scaling effectively to resolutions beyond the training data.
This paper presents a groundbreaking 3D-aware image generation technique that employs 2D diffusion models in a multiview 2D image set generation framework. By incorporating depth information and training on large-scale datasets like ImageNet, the method achieves superior image quality and generates images with large view angles from diverse real-world environments.
Chroma provides an open-source embeddings database.
Two profiles of OpenAI CEO Sam Altman that dives into his thoughts on the rapid progress of AI.
A job board of prompt engineering jobs.
Zoom is expanding its AI capabilities by incorporating proprietary AI models and leveraging external models from companies like OpenAI. The next-gen AI smart companion, Zoom IQ, aims to empower collaboration and streamline workflows by summarizing chat threads, drafting content, and creating meeting agendas. As these features roll out to select customers, Zoom IQ aims to save time and enhance creativity for individuals and teams.
The paper presents a new approach called HiPer, which uses highly personalized text embedding to achieve personalization and content manipulation in image generation and manipulation without the need for model fine-tuning or identifiers. The approach enables manipulation of background, texture, and motion with just a single image and target text, producing highly personalized and complex semantic image edits across a wide range of tasks. The authors believe that their novel understanding of the text embedding space has the potential to inspire further research across various tasks.
Player pianos have existed since before computers, but now a computer can play the piano by controlling robotic fingers. This is a fascinating control problem that allows an RL agent to learn how to control dexterous fingers to play piano. There is an interactive demo that lets you poke and prod the hands while they play.
DreamBooth3D is a new approach that personalizes text-to-3D generative models with as few as 3-6 casually captured images of a subject. The method combines recent advances in personalizing text-to-image models with text-to-3D generation, overcoming the issue of overfitting to input viewpoints through a 3-stage optimization strategy. DreamBooth3D can produce high-quality, subject-specific 3D assets with text-driven modifications such as novel poses, colors, and attributes that are not seen in any of the input images of the subject.
This repo proposes LLaMA-Adapter, a lightweight adaption method for fine-tuning instruction-following LLaMA models, using 52K data provided by Stanford Alpaca.
This study presents a novel diffusion setup that addresses the challenges of data scarcity and computational complexity in 3D generative modeling by utilizing posed 2D images and decoupling model memory from spatial memory. The approach demonstrates scalability, robust training, and competitive sample quality when applied to the CO3D dataset.
Xturing provides fast, efficient, and simple fine-tuning of LLMs.
Following the virality of some AI-generated images and a surge in new users, Midjourney is discontinuing free trials.
Researchers address limitations in pretrained diffusion models for image editing by optimizing the input of the value linear network in cross-attention layers and introducing attention regularization to maintain object-like attention maps. These improvements result in superior editing capabilities, demonstrated through extensive experimental prompt-editing on various images.
GPT4All is a 7B parameter language model, trained on high-quality assistant data and accompanied by 800K data samples, training code, and model weights for open research and reproducibility. A 4-bit quantized version that can run on a CPU is also provided.
Since the advent of powerful text-to-image models last year, people have been trying to move from 2d to 3d generation. One issue is the “multi-view consistency” problem, where if you only see part of an object, how do you infer what the other parts look like. Many naive methods will copy and paste the 2d image around the object which results in very odd images. By using a consistency injection technique, you can leverage the semantic understanding from 2d models to create fairly robust 3d models. The text to 3d printing pipeline is coming!
The authors present VIVE3D, a new technique that extends the capabilities of image-based 3D GANs to video editing. It uses a novel GAN inversion technique optimized for camera parameters and introduces new edits that show novel views of the head using optical flow-guided compositing to combine the head with the background video, resulting in high-fidelity, consistent quality face edits from various camera viewpoints.
Instruct 3D-to-3D, a high-quality 3D-to-3D conversion method, leverages pretrained Image-to-Image diffusion models to transform 3D scenes based on text instructions while maintaining consistency and controllability. Quantitative and qualitative evaluations demonstrate superior conversion quality compared to baseline methods, with the addition of dynamic scaling for adjustable geometry transformation intensity.
GPT4All provides demos, data, and code to train an assistant-style LLM.
Lit-LLaMA is a fully open source model of LLaMA.
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AI content needs a human touch. Cohesive
Google is teaming up with Replit to combine its AI language models with Replit's Ghostwriter software, which helps programmers write code. This collaboration aims to compete with Microsoft's GitHub Copilot, which uses OpenAI's language-generating AI. Ghostwriter will use Google's AI to improve code suggestions, program completion, and answering developer questions. Replit plans to enhance its software by offering end-to-end support for code writing, testing, deployment, app review, and collaboration. The partnership will also see Replit expand its use of Google's cloud services and distribution of its software.
Last year Deepmind published research on their Flamingo model which is a powerful multi-modal text and vision model. This model could answer questions about images while still being a powerful text model. Laion has now released a framework for training Flamingo models and a 9B parameter checkpoint trained on 5M samples of the Multimodal C4 dataset and 10M samples from LAION-2B image dataset. The model is not usable for commercial applications as it uses the 7B Llama checkpoint from Meta. It also doesn’t yet match Deepmind’s Flamingo on benchmark performance.
One of the startup leaders in AI enabled search has launched an iPhone app and raised additional funding. They’re aiming to be the leader in knowledge discovery. We’ll see which of these services find differentiation, but the 2 million users in just a few months is impressive growth.
NLP needs data, this data is usually labeled by crowdsourced workers on various platforms. It turns out, maybe unsurprisingly, that gpt-3.5-turbo is extremely good at following instructions for labeling and often outperforms human annotators on relevance, stance, topic, and other important measures. Even more importantly, it also costs much less (about 20x) than hiring human annotators.
The paper introduces EVA-CLIP, a set of models that significantly improve the efficiency and effectiveness of CLIP training. EVA-CLIP achieves superior performance compared to previous models with the same number of parameters but smaller training costs, and the complete suite is released for open research.
Imagen, a text-to-image diffusion model, rivals CLIP in zero-shot classification, outperforming in shape/texture bias tests and suggesting generative pre-training as a valuable approach in vision-language tasks.
This paper discusses how text-to-image diffusion models, like DreamBooth, can generate realistic images from simple text inputs, but can also be misused to produce fake news or disturbing content targeting individuals. The paper proposes a defense system called Anti-DreamBooth that adds subtle noise perturbations to user images to disrupt the generation quality of any DreamBooth model trained on them, and evaluates its effectiveness against a wide range of algorithms on various text-to-image model versions.
AIx is a CLI tool to interact with LLM APIs.