🏅🏅🏅 What is trending in AI research- Apple AI Research Introduces AIM + Meta and NYU Researchers Introduce Self-Rewarding Language Models .... and many more...

Hi there, 

I hope you all are doing well!

Here are this week's top AI/ML research briefs.

Apple AI Research Introduces AIM: A Collection of Vision Models Pre-Trained with an Autoregressive Objective 🏅
🤔 How can we enhance vision models to achieve remarkable performance, akin to their Large Language Model (LLM) counterparts? 🌟 This paper introduces AIM, a groundbreaking collection of vision models pre-trained using an autoregressive objective. 🚀 Like LLMs, these models scale beautifully in performance with increased model capacity and data volume. The paper reveals two key insights: (1) the visual features' performance boosts with the model size and data quantity, and (2) the model's objective function score directly links to its success in downstream tasks. 🌐 Showcasing this, a 7 billion parameter AIM model, fed with a massive 2 billion images, astoundingly scores 84.0% on ImageNet-1k with a frozen trunk. Here's the kicker: there's no performance plateau even at this scale, hinting that AIM could be the new frontier in large-scale vision model training. 🎉 Best part? AIM's pre-training mirrors LLMs and smoothly scales without needing any special tweaks for image processing. 📈 A true leap in AI vision! 🎨👁️✨

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DeepSeek-AI Proposes DeepSeekMoE: An Innovative Mixture-of-Experts (MoE) Language Model Architecture Specifically Designed Towards Ultimate Expert Specialization 🏅
How can we make large language models more efficient without losing their power? 🤔💡 This paper tackles this by introducing the innovative DeepSeekMoE architecture, a fresh take on the Mixture-of-Experts (MoE) approach. Unlike traditional MoE models like GShard, which selects the top-K experts from a set of N, DeepSeekMoE steps it up! 🚀 It segments experts even further, allowing for a wider variety of expert combinations and dedicates a portion of these experts to shared, common knowledge, reducing overlap and redundancy. The magic? 🌟 With only 2B parameters, DeepSeekMoE matches GShard's 2.9B in performance and nearly rivals its dense model equivalent. It's like getting a sports car's performance with a compact car's efficiency! 🏎️💨 Scaling it up, DeepSeekMoE with 16B parameters performs as well as LLaMA2 7B but with almost half the computational load. And the cherry on top? 🍒 At 145B parameters, it holds its own against DeepSeek 67B, but with significantly less computational demand (only 28.5% or perhaps even 18.2%). In essence, DeepSeekMoE is a game-changer in AI efficiency! 🌍🤖🔋

Researchers from the University of Washington and Allen Institute for AI Present Proxy-Tuning: An Efficient Alternative to Finetuning Large Language Models 🏅
🌟 How can we effectively adapt large pretrained language models to desired behaviors without the heavy resource requirements or access to private model weights? The researchers from the University of Washington and Allen Institute for AI present proxy-tuning, a decoding-time algorithm designed to fine-tune large black-box language models (LMs) without accessing their internal weights. This method leverages a smaller tuned LM and computes the difference between its predictions and the untuned version. Using decoding-time experts, the original predictions of the larger base model are adjusted based on this difference, effectively achieving the benefits of direct tuning.

Proxy-tuning aims to bridge the disparity between a base language model and its directly tuned version without altering the base model’s parameters. This approach includes tuning a smaller LM and using the contrast between its predictions and the untuned version to adjust the original predictions of the base model toward the tuning direction. Importantly, proxy-tuning preserves the advantages of extensive pretraining while effectively achieving the desired behaviors in the language model.

This AI Paper from Meta and NYU Introduces Self-Rewarding Language Models that are Capable of Self-Alignment via Judging and Training on their Own Generations 🏅
Meta and New York University researchers have proposed a novel approach called Self-Rewarding Language Models, aiming to overcome bottlenecks in traditional methods. Unlike frozen reward models, their process involves training a self-improving reward model that is continuously updated during LLM alignment. By integrating instruction-following and reward modeling into a single system, the model generates and evaluates its examples, refining instruction-following and reward modeling abilities.

Self-Rewarding Language Models start with a pretrained language model and a limited set of human-annotated data. The model is designed to simultaneously excel in two key skills: i) instruction following and ii) self-instruction creation. The model self-evaluates generated responses through the LLM-as-a-Judge mechanism, eliminating the need for an external reward model. The iterative self-alignment process involves developing new prompts, evaluating responses, and updating the model using AI Feedback Training. This approach enhances instruction following and improves the model’s reward modeling ability over successive iterations, deviating from traditional fixed reward models.

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Other Trending Papers 🏅🏅🏅

• Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads [Paper]

• CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark [Paper]

• Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data [Paper]

• WARM: On the Benefits of Weight Averaged Reward Models [Paper]

• Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text [Paper]

Take the project management out of product building.

When you started building your product, you didn’t dream of the endless admin and organization tasks needed to keep your projects on track. You just wanted to make something that people would love.

Though project management is necessary, gone are the days of you spending hours on triaging bugs, restacking priorities, updating statuses, and more.

Height is the AI project collaboration tool that handles the mental legwork of project management for you invisibly, automatically, and autonomously — all so you can focus your energy on building a successful product.

Join the waitlist — be the first to access Height 2.0

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