Chain of Hindsight Aligns Language Models with Feedback
AI language models are becoming a major part of our digital world. The challenge, however, lies in aligning these models with human preferences to be genuinely useful and valuable. Current methods, although successful in many ways, have limitations - they are either inefficient in utilizing data or depend heavily on challenging reward functions and reinforcement learning.
Here comes "Chain of Hindsight," an exciting, novel technique inspired by human learning mechanisms. It can learn from any form of feedback, even transforming it into language for fine-tuning the model. This approach conditions the model on a sequence of model generations paired with feedback, helping it learn to correct negative attributes or errors. It is significantly outperforming previous methods, particularly showing major strides in summarization and dialogue tasks.
Paper link: https://arxiv.org/abs/2302.02676
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-coh
#deeplearning #nlp #llm
AI language models are becoming a major part of our digital world. The challenge, however, lies in aligning these models with human preferences to be genuinely useful and valuable. Current methods, although successful in many ways, have limitations - they are either inefficient in utilizing data or depend heavily on challenging reward functions and reinforcement learning.
Here comes "Chain of Hindsight," an exciting, novel technique inspired by human learning mechanisms. It can learn from any form of feedback, even transforming it into language for fine-tuning the model. This approach conditions the model on a sequence of model generations paired with feedback, helping it learn to correct negative attributes or errors. It is significantly outperforming previous methods, particularly showing major strides in summarization and dialogue tasks.
Paper link: https://arxiv.org/abs/2302.02676
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-coh
#deeplearning #nlp #llm
QLoRA: Efficient Finetuning of Quantized LLMs
Thia paper introduces QLoRA, a novel finetuning approach that decreases memory usage significantly, while maintaining impressive performance. Imagine this - a 65 billion parameter model finetuned on a single 48GB GPU, while preserving full 16-bit task performance. This method involves backpropagating gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters, a method that opens up new frontiers in machine learning. The icing on the cake is their high-performing model family, Guanaco, which trumps all previously released models on the Vicuna benchmark, achieving a staggering 99.3% of the performance level of ChatGPT with just 24 hours of finetuning on a single GPU.
The study also unveils several innovative techniques to conserve memory without compromising performance. These include 4-bit NormalFloat (NF4), an innovative data type that is theoretically optimal for normally distributed weights, double quantization for average memory footprint reduction, and paged optimizers to handle memory spikes. The QLoRA approach was applied to finetune more than 1000 models, leading to a detailed analysis of instruction following and chatbot performance across various model types and scales. The results affirm that QLoRA finetuning on a small, high-quality dataset yields state-of-the-art results, even with smaller models than previously used. A notable finding is that GPT-4 evaluations offer a cost-effective alternative to human evaluation. All models and code, including CUDA kernels for 4-bit training, have been released by the researchers.
Paper link: https://arxiv.org/abs/2305.14314
Code link: https://github.com/artidoro/qlora
CUDA kernels link: https://github.com/TimDettmers/bitsandbytes
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-qlora
#deeplearning #nlp #llm #quantization
Thia paper introduces QLoRA, a novel finetuning approach that decreases memory usage significantly, while maintaining impressive performance. Imagine this - a 65 billion parameter model finetuned on a single 48GB GPU, while preserving full 16-bit task performance. This method involves backpropagating gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters, a method that opens up new frontiers in machine learning. The icing on the cake is their high-performing model family, Guanaco, which trumps all previously released models on the Vicuna benchmark, achieving a staggering 99.3% of the performance level of ChatGPT with just 24 hours of finetuning on a single GPU.
The study also unveils several innovative techniques to conserve memory without compromising performance. These include 4-bit NormalFloat (NF4), an innovative data type that is theoretically optimal for normally distributed weights, double quantization for average memory footprint reduction, and paged optimizers to handle memory spikes. The QLoRA approach was applied to finetune more than 1000 models, leading to a detailed analysis of instruction following and chatbot performance across various model types and scales. The results affirm that QLoRA finetuning on a small, high-quality dataset yields state-of-the-art results, even with smaller models than previously used. A notable finding is that GPT-4 evaluations offer a cost-effective alternative to human evaluation. All models and code, including CUDA kernels for 4-bit training, have been released by the researchers.
Paper link: https://arxiv.org/abs/2305.14314
Code link: https://github.com/artidoro/qlora
CUDA kernels link: https://github.com/TimDettmers/bitsandbytes
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-qlora
#deeplearning #nlp #llm #quantization
Forwarded from ml4se
CodeTF: One-stop Transformer Library for State-of-the-art Code LLM (Salesforce)
The authors we present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. CodeTF is designed with a unified interface to enable rapid access and development across different types of models, datasets and tasks. The library supports a collection of pretrained Code LLM models and popular code benchmarks, including a standardized interface to train and serve code LLMs efficiently, and data features such as language-specific parsers and utility functions for extracting code attributes.
The authors we present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. CodeTF is designed with a unified interface to enable rapid access and development across different types of models, datasets and tasks. The library supports a collection of pretrained Code LLM models and popular code benchmarks, including a standardized interface to train and serve code LLMs efficiently, and data features such as language-specific parsers and utility functions for extracting code attributes.
The effectiveness of MAE pre-pretraining for billion-scale pretraining
Revolutionizing the current pretrain-then-finetune paradigm of computer vision, this research has introduced an innovative pre-pretraining stage. Utilizing the Masked Autoencoder (MAE) technique for model initialization, this pre-pretraining strategy scales with the size of both the model and the data. This makes it an ideal tool for training next-generation foundation models, even on the grandest scales.
The robustness of our pre-pretraining technique is demonstrated by consistent improvement in model convergence and downstream transfer performance across diverse model scales and dataset sizes. The authors measured the effectiveness of pre-pretraining on a wide array of visual recognition tasks, and the results have been promising. The ielargest model achieved unprecedented results on iNaturalist-18 (91.3%), 1-shot ImageNet-1k (62.1%), and zero-shot transfer on Food-101 (96.0%), underlining the tremendous potential of proper model initialization, even when handling web-scale pretraining with billions of images.
Paper link: https://arxiv.org/abs/2303.13496
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-mae-pretrain
#deeplearning #cv #pretraining #selfsupervisedlearning
Revolutionizing the current pretrain-then-finetune paradigm of computer vision, this research has introduced an innovative pre-pretraining stage. Utilizing the Masked Autoencoder (MAE) technique for model initialization, this pre-pretraining strategy scales with the size of both the model and the data. This makes it an ideal tool for training next-generation foundation models, even on the grandest scales.
The robustness of our pre-pretraining technique is demonstrated by consistent improvement in model convergence and downstream transfer performance across diverse model scales and dataset sizes. The authors measured the effectiveness of pre-pretraining on a wide array of visual recognition tasks, and the results have been promising. The ielargest model achieved unprecedented results on iNaturalist-18 (91.3%), 1-shot ImageNet-1k (62.1%), and zero-shot transfer on Food-101 (96.0%), underlining the tremendous potential of proper model initialization, even when handling web-scale pretraining with billions of images.
Paper link: https://arxiv.org/abs/2303.13496
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-mae-pretrain
#deeplearning #cv #pretraining #selfsupervisedlearning
StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners
In a ground-breaking exploration of visual representation learning, researchers have leveraged synthetic images produced by leading text-to-image models, specifically Stable Diffusion, achieving promising results. The study uncovers two key insights - firstly, when configured correctly, self-supervised methods trained on synthetic images can match or even outperform those trained on real images. This suggests an exciting avenue for efficient and effective representation learning, reducing the need for extensive real image datasets.
Secondly, the researchers have devised a novel approach called StableRep, a multi-positive contrastive learning method that treats multiple images, generated from the same text prompt, as mutual positives. The compelling finding is that StableRep, trained solely with synthetic images, outperforms representations learned by prominent methods such as SimCLR and CLIP, even when these used real images. In a striking demonstration, when language supervision is added, StableRep trained with 20M synthetic images outperforms CLIP trained with a whopping 50M real images. These findings not only underscore the potential of synthetic data but also pave the way for more efficient, large-scale visual representation learning.
Paper link: https://arxiv.org/abs/2306.00984
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-stablerep
#deeplearning #cv #nlp #stablediffusion #texttoimage #syntheticdata
In a ground-breaking exploration of visual representation learning, researchers have leveraged synthetic images produced by leading text-to-image models, specifically Stable Diffusion, achieving promising results. The study uncovers two key insights - firstly, when configured correctly, self-supervised methods trained on synthetic images can match or even outperform those trained on real images. This suggests an exciting avenue for efficient and effective representation learning, reducing the need for extensive real image datasets.
Secondly, the researchers have devised a novel approach called StableRep, a multi-positive contrastive learning method that treats multiple images, generated from the same text prompt, as mutual positives. The compelling finding is that StableRep, trained solely with synthetic images, outperforms representations learned by prominent methods such as SimCLR and CLIP, even when these used real images. In a striking demonstration, when language supervision is added, StableRep trained with 20M synthetic images outperforms CLIP trained with a whopping 50M real images. These findings not only underscore the potential of synthetic data but also pave the way for more efficient, large-scale visual representation learning.
Paper link: https://arxiv.org/abs/2306.00984
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-stablerep
#deeplearning #cv #nlp #stablediffusion #texttoimage #syntheticdata
BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks
Introducing the groundbreaking Biomedical Generative Pre-trained Transformer (BiomedGPT) model, this paper revolutionizes the field of biomedicine by offering a unified and generalist approach. BiomedGPT harnesses the power of self-supervision on extensive and diverse datasets, enabling it to effortlessly handle multi-modal inputs and excel in a wide range of downstream tasks. In a series of comprehensive experiments, BiomedGPT astoundingly outperforms its predecessors, emerging as the unrivaled leader across five distinct tasks and a staggering 20 public datasets encompassing over 15 unique biomedical modalities. Its ability to deliver expansive and all-encompassing representations of biomedical data heralds a significant advancement in the field, with promising implications for improving healthcare outcomes.
Through meticulous ablation studies, the efficacy of BiomedGPT's multi-modal and multi-task pretraining approach is vividly showcased. This groundbreaking model effortlessly transfers its vast knowledge to previously unseen data, demonstrating its versatility and adaptability. The implications of this research are profound, paving the way for the development of unified and all-encompassing models for biomedicine.
Paper link: https://arxiv.org/abs/2305.17100
Code link: https://github.com/taokz/BiomedGPT
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-biomedgpt
#deeplearning #nlp #selfsupervised #gpt #biomedicine
Introducing the groundbreaking Biomedical Generative Pre-trained Transformer (BiomedGPT) model, this paper revolutionizes the field of biomedicine by offering a unified and generalist approach. BiomedGPT harnesses the power of self-supervision on extensive and diverse datasets, enabling it to effortlessly handle multi-modal inputs and excel in a wide range of downstream tasks. In a series of comprehensive experiments, BiomedGPT astoundingly outperforms its predecessors, emerging as the unrivaled leader across five distinct tasks and a staggering 20 public datasets encompassing over 15 unique biomedical modalities. Its ability to deliver expansive and all-encompassing representations of biomedical data heralds a significant advancement in the field, with promising implications for improving healthcare outcomes.
Through meticulous ablation studies, the efficacy of BiomedGPT's multi-modal and multi-task pretraining approach is vividly showcased. This groundbreaking model effortlessly transfers its vast knowledge to previously unseen data, demonstrating its versatility and adaptability. The implications of this research are profound, paving the way for the development of unified and all-encompassing models for biomedicine.
Paper link: https://arxiv.org/abs/2305.17100
Code link: https://github.com/taokz/BiomedGPT
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-biomedgpt
#deeplearning #nlp #selfsupervised #gpt #biomedicine
🇵🇹Are there people in Lissabon? Let’s meet for a brunch this week!
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
In a recent breakthrough, a novel approach for learning highly semantic image representations has been introduced that eschews the need for hand-crafted data augmentations. The strategy, known as Image-based Joint-Embedding Predictive Architecture (I-JEPA), offers a refreshing, non-generative pathway to self-supervised learning from images. The concept underpinning I-JEPA is deceptively simple, yet incredibly powerful: it takes a single context block from an image and predicts the representations of various target blocks within the same image.
I-JEPA's core design principle - its masking strategy - plays a pivotal role in shaping the system's semantic prowess. The key is to sample target blocks at a sufficiently large, semantic scale while using a context block that provides ample, spatially distributed information. When integrated with Vision Transformers, I-JEPA exhibits impressive scalability. To illustrate, a ViT-Huge/14 model was trained on ImageNet using just 16 A100 GPUs in under 72 hours, delivering robust performance across a wide spectrum of tasks, including linear classification, object counting, and depth prediction.
Paper link: https://arxiv.org/abs/2301.08243
Code link: https://github.com/facebookresearch/ijepa
Blogpost link: https://ai.facebook.com/blog/yann-lecun-ai-model-i-jepa/
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-ijepa
#deeplearning #cv #selfsupervisedlearning
In a recent breakthrough, a novel approach for learning highly semantic image representations has been introduced that eschews the need for hand-crafted data augmentations. The strategy, known as Image-based Joint-Embedding Predictive Architecture (I-JEPA), offers a refreshing, non-generative pathway to self-supervised learning from images. The concept underpinning I-JEPA is deceptively simple, yet incredibly powerful: it takes a single context block from an image and predicts the representations of various target blocks within the same image.
I-JEPA's core design principle - its masking strategy - plays a pivotal role in shaping the system's semantic prowess. The key is to sample target blocks at a sufficiently large, semantic scale while using a context block that provides ample, spatially distributed information. When integrated with Vision Transformers, I-JEPA exhibits impressive scalability. To illustrate, a ViT-Huge/14 model was trained on ImageNet using just 16 A100 GPUs in under 72 hours, delivering robust performance across a wide spectrum of tasks, including linear classification, object counting, and depth prediction.
Paper link: https://arxiv.org/abs/2301.08243
Code link: https://github.com/facebookresearch/ijepa
Blogpost link: https://ai.facebook.com/blog/yann-lecun-ai-model-i-jepa/
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-ijepa
#deeplearning #cv #selfsupervisedlearning
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
AI-assistant agents like ChatGPT have largely depended on supervised fine-tuning and reinforcement learning from human feedback. But, this method brings its own set of challenges - high costs, potential biases, and constraints on the true potential of these AI agents. What if there was a more effective, self-sufficient way to align AI output with human intentions? Enter Self-ALIGN, a groundbreaking methodology that marries principle-driven reasoning and the generative capabilities of large language models. This promising approach takes the AI realm by storm, offering a novel way to ensure our AI models are more helpful, ethical, and reliable - all with minimal human intervention.
Self-ALIGN is a multistage process that works by generating synthetic prompts from a large language model, augmenting prompt diversity, and leveraging a concise set of human-written principles to guide AI models. When applied to the LLaMA-65b base language model, it led to the creation of a new AI assistant, Dromedary, using less than 300 lines of human annotations. Dromedary not only outshines several state-of-the-art AI systems, such as Text-Davinci-003 and Alpaca, but it does so on a variety of benchmark datasets.
Paper link: https://arxiv.org/abs/2305.03047
Code link: https://mitibmdemos.draco.res.ibm.com/dromedary
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-dromedary
#deeplearning #nlp #llm
AI-assistant agents like ChatGPT have largely depended on supervised fine-tuning and reinforcement learning from human feedback. But, this method brings its own set of challenges - high costs, potential biases, and constraints on the true potential of these AI agents. What if there was a more effective, self-sufficient way to align AI output with human intentions? Enter Self-ALIGN, a groundbreaking methodology that marries principle-driven reasoning and the generative capabilities of large language models. This promising approach takes the AI realm by storm, offering a novel way to ensure our AI models are more helpful, ethical, and reliable - all with minimal human intervention.
Self-ALIGN is a multistage process that works by generating synthetic prompts from a large language model, augmenting prompt diversity, and leveraging a concise set of human-written principles to guide AI models. When applied to the LLaMA-65b base language model, it led to the creation of a new AI assistant, Dromedary, using less than 300 lines of human annotations. Dromedary not only outshines several state-of-the-art AI systems, such as Text-Davinci-003 and Alpaca, but it does so on a variety of benchmark datasets.
Paper link: https://arxiv.org/abs/2305.03047
Code link: https://mitibmdemos.draco.res.ibm.com/dromedary
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-dromedary
#deeplearning #nlp #llm
Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale
In the ever-evolving field of natural language processing and computer vision research, the revolution is being led by large-scale generative models like GPT and DALL-E. These models have the remarkable capability of generating high fidelity text or image outputs, and more importantly, they possess a 'generalist' character, able to solve tasks that they weren't explicitly trained to accomplish. However, when it comes to speech generative models, there's still a significant gap in terms of scale and task generalization. Enter, Voicebox - a pioneering advancement set to redefine the landscape of speech generation technology.
Voicebox is an exceptionally versatile text-guided generative model for speech at an impressive scale. Trained on over 50K hours of unfiltered, unenhanced speech data, Voicebox is a non-autoregressive flow-matching model, designed to infill speech, given an audio context and text. Much like its predecessors, Voicebox is able to perform a wide range of tasks through in-context learning, but with an added flexibility - it can condition on future context. The applications are boundless - from mono or cross-lingual zero-shot text-to-speech synthesis to noise removal, content editing, style conversion, and diverse sample generation. What's truly phenomenal is Voicebox's capability to outshine the state-of-the-art zero-shot TTS model, VALL-E, on both intelligibility and audio similarity metrics, while being a staggering 20 times faster.
Paper link: https://research.facebook.com/publications/voicebox-text-guided-multilingual-universal-speech-generation-at-scale/
Blogpost link: https://ai.facebook.com/blog/voicebox-generative-ai-model-speech/
Project link: https://voicebox.metademolab.com/
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-voicebox
#deeplearning #nlp #speechgeneration #texttospeech
In the ever-evolving field of natural language processing and computer vision research, the revolution is being led by large-scale generative models like GPT and DALL-E. These models have the remarkable capability of generating high fidelity text or image outputs, and more importantly, they possess a 'generalist' character, able to solve tasks that they weren't explicitly trained to accomplish. However, when it comes to speech generative models, there's still a significant gap in terms of scale and task generalization. Enter, Voicebox - a pioneering advancement set to redefine the landscape of speech generation technology.
Voicebox is an exceptionally versatile text-guided generative model for speech at an impressive scale. Trained on over 50K hours of unfiltered, unenhanced speech data, Voicebox is a non-autoregressive flow-matching model, designed to infill speech, given an audio context and text. Much like its predecessors, Voicebox is able to perform a wide range of tasks through in-context learning, but with an added flexibility - it can condition on future context. The applications are boundless - from mono or cross-lingual zero-shot text-to-speech synthesis to noise removal, content editing, style conversion, and diverse sample generation. What's truly phenomenal is Voicebox's capability to outshine the state-of-the-art zero-shot TTS model, VALL-E, on both intelligibility and audio similarity metrics, while being a staggering 20 times faster.
Paper link: https://research.facebook.com/publications/voicebox-text-guided-multilingual-universal-speech-generation-at-scale/
Blogpost link: https://ai.facebook.com/blog/voicebox-generative-ai-model-speech/
Project link: https://voicebox.metademolab.com/
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-voicebox
#deeplearning #nlp #speechgeneration #texttospeech
