Tag: large language model

AI Machine Learning & Data Science Research

DeepMind’s DiLoCo Revolutionizes Language Model Training with 500× Less Communication

In a new paper DiLoCo: Distributed Low-Communication Training of Language Models, a Google DeepMind research team presents Distributed Low-Communication (DiLoCo). DiLoCo employs a distributed optimization algorithm that facilitates the training of language models on islands of poorly connected devices, surpassing the performance of fully synchronous models while reducing communication by 500 times.

AI Machine Learning & Data Science Research

Apple Repurposes Large Language Models for Reinforcement Learning challenges in Embodied AI

An Apple research team presents Large LAnguage model Reinforcement Learning Policy (LLaRP). LLaRP effectively repurposes LLMs for Reinforcement Learning (RL) challenges within the realm of Embodied Artificial Intelligence (AI), achieving a remarkable 1.7 times higher success rate compared to other established baselines and zero-shot LLM applications.

AI Machine Learning & Data Science Nature Language Tech Research

The Reversal Curse: Uncovering the Intriguing Limits of Language Models

In a new paper titled “The Reversal Curse: LLMs trained on ‘A is B’ fail to learn ‘B is A'” authored by a collaborative research team from Vanderbilt University, the UK Frontier AI Taskforce, Apollo Research, New York University, the University of Sussex, and the University of Oxford, has unveiled a remarkable shortcoming in auto-regressive large language models (LLMs).

AI Machine Learning & Data Science Nature Language Tech Research

Unveiling the Enigma: Meta AI & UPC Decodes the Inner Workings of Large Scale Language Models

In a new paper Neurons in Large Language Models: Dead, N-gram, Positional, a research team from Meta AI and Universitat Politècnica de Catalunya conducts comprehensive analysis of a family of Open Pre-trained Transformer Language Models (OPT) up to 66b parameters to provide insights of how feed-forward network (FFN) layers act.

AI Machine Learning & Data Science Research

CMU & Tsinghua U’s Prompt2Model Generates Deployable Models Following Natural Language Instructions

In a new paper Prompt2Model: Generating Deployable Models from Natural Language Instructions, a research team from Carnegie Mellon University and Tsinghua University introduces Prompt2Model, a general-purpose approach that is able to use prompting technique to specify system behavior while resulting in a deployable special purpose model that enjoys all the advantages thereof.

AI Machine Learning & Data Science Research

Boston U’s Platpus Provides Quick, Cheap, and Powerful Refinement of LLMs, Achieving Top 1 in Open LLM Leaderboard

In a new paper Platypus: Quick, Cheap, and Powerful Refinement of LLMs, a Boston University research team presents Platpus, a family of fine-tuned and merged Large Language Models (LLMs) that achieves the first place in HuggingFace’s Open LLM Leaderboard by performing quick, cheap and powerful refinement of conventional LLMs.

AI Machine Learning & Data Science Research

New Study Unleashes The Power of Large Language Models to Master 16000+ Real World APIs

In a new paper ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs, a research team from Tsinghua University, ModelBest Inc., Renmin University of China, Yale University, Tencent Inc. and Zhihu Inc. presents ToolLLM, a general tool-use framework that demonstrates a compelling capability to master 16464 real-world RESTful APIs

AI Machine Learning & Data Science Research

65-Billion-Parameter Large Model Pretraining Accelerated by 38%, Best Practices for Building LLaMA-like Base Models Open-Source

Colossal-AI—the world’s largest and most active big model development tool and community—utilizes the current most widely used large model, LLaMA, to provide an example of the tool’s groundbreaking pre-training solutions for the 65 billion parameter large model which improves the training speed by 38%.

AI Machine Learning & Data Science Research

DeepMind Collaborates on Shaping Personality Traits in LLMs

In a new paper Personality Traits in Large Language Models, a research team from Google, Cambridge University and Keio University proposes principled, validated methods to construct validity of characterizing personalities in LLM, simulates population variance in LLM responses and develops a personality shaping mechanism to control LLM personality traits.

AI Machine Learning & Data Science Research

DeepMind’s Proposes New Paradigm for Interfacing Language Model with Robots Through Rewards

In a new paper Language to Rewards for Robotic Skill Synthesis, a Google DeepMind research team proposes a new paradigm to leverage reward functions to interface language and low-level robot actions, which enables non-technical users to steer novel and intricate robot actions without large amount of data or expert knowledge to engineer low-level primitives.

AI Machine Learning & Data Science Research

Salesforce AI’s CodeTF Library Facilitates Easy LLM Integration for Code Intelligence Tasks

In a new paper CodeTF: One-stop Transformer Library for State-of-the-art Code LLM, a Salesforce AI research team develop CodeTF, an open-source one-stop comprehensive Python library that provides a seamless interface for training and inferencing on code intelligence tasks, aiming to facilitate easy integration of state-of-the-art language models into real-world applications.

AI Machine Learning & Data Science Research

Google & Stanford U’s DoReMi Significantly Speeds Up Language Model Pretraining

In the new paper DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining, a research team from Google and Stanford University introduces Domain Reweighting with Minimax Optimization (DoReMi), a domain weight optimization strategy that leverages distributionally robust optimization (DRO) to substantially speed up effective language model pretraining.

AI Machine Learning & Data Science Research

Alibaba & HUST’s ONE-PEACE: Toward a General Representation Model For Unlimited Modalities

In the new paper ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities, a research team from Alibaba Group’s DAMO Academy and the Huazhong University of Science and Technology releases ONE-PEACE, a highly extensible model that can align and integrate representations across vision, audio, and language modalities; opening a path toward the creation of a general representation model for unlimited modalities.

AI Machine Learning & Data Science Research

Salesforce AI’s CodeT5+ Open Code LLMs Flexibly Adapt to Diverse Downstream Code Understanding and Generation Tasks

In the new paper CodeT5+: Open Code Large Language Models for Code Understanding and Generation, a Salesforce AI Research team presents CodeT5+, a novel family of encoder-decoder code foundation large language models that can be flexibly adapted to a wide range of code understanding and generation tasks and outperform various code-related benchmarks.

AI Machine Learning & Data Science Nature Language Tech Research

‘May the Source Be With You!’ – BigCode’s Open-Access StarCoder Outperforms All Existing Open Code LLMs

In the new paper StarCoder: May the Source Be With You!, the BigCode community releases StarCoder and StarCoderBase, 15.5B parameter open-access large language models (LLMs) trained on 80+ programming languages. StarCoderBase outperforms all multi-programming-language code LLMs, and StarCoder surpasses all models fine-tuned on Python.

AI Machine Learning & Data Science Research

Meet VideoChat: Integrating Language and Video Models to Boost Video Understanding

In the new paper VideoChat: Chat-Centric Video Understanding, a research team from Shanghai AI Laboratory, Nanjing University, the University of Hong Kong, and the Chinese Academy of Sciences presents VideoChat, a groundbreaking end-to-end chat-centric video understanding system that leverages state-of-the-art video and language models to improve spatiotemporal reasoning, event localization, and causal relationship inference.

AI Machine Learning & Data Science Research

Microsoft’s Automatic Prompt Optimization Improves Prompts to Boost LLM Performance

In the new paper Automatic Prompt Optimization with “Gradient Descent” and Beam Search, a Microsoft research team presents Automatic Prompt Optimization, a simple and general prompt optimization algorithm that automatically improves prompts for large language models, significantly reducing the time and energy spent on manual prompting approaches.

AI Machine Learning & Data Science Research

Optimizing Transformers: Microsoft & RUC’s ResiDual Solves Gradient Vanishing and Representation Collapse Issues

In the new paper ResiDual: Transformer With Dual Residual Connections, a team from Microsoft Research, Microsoft Azure Translation, and Renmin University of China proposes ResiDual, a novel transformer architecture that fuses the connections in post-layer normalization and pre-layer normalization to exploit the benefits of both while also addressing their limitations.

AI Machine Learning & Data Science Nature Language Tech Research

Microsoft’s LLMA Accelerates LLM Generations via an ‘Inference-With-Reference’ Decoding Approach

In the new paper Inference with Reference: Lossless Acceleration of Large Language Models, a Microsoft research team proposes LLMA, an inference-with-reference decoding mechanism that achieves up to 2x lossless speed-ups with identical generation results by exploiting the overlaps between LLM outputs and references.