AI Technology & Industry Review
In a new paper SpeechVerse: A Large-scale Generalizable Audio Language Model, a research team from Amazon AWS AI Labs introduces SpeechVerse, a robust multi-task framework that leverages supervised instruction fine-tuning to achieve strong performance across various speech tasks.
In a new paper NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment, a team of researchers from Nvidia introduces NeMo-Aligner, a toolkit designed for large-scale LLM model alignment that can efficiently harness the power of hundreds of GPUs for training.
In a new paper TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding, a research team from CMU and Meta introduces TriForce—a hierarchical speculative decoding system tailored for scalable long sequence generation, reaching up to 2.31× on an A100 GPU.
In a new paper NExT: Teaching Large Language Models to Reason about Code Execution, a Google DeepMind research team proposes Naturalized Execution Tuning (NExT), a method aims to equip LLMs with the ability to scrutinize program execution traces and deduce runtime behaviors through chain-of-thought (CoT) rationales.
In a new paper LM Transparency Tool: Interactive Tool for Analyzing Transformer Language Models, a research team from Meta, University College London and Universitat Politècnica de Catalunya introduces the LM Transparency Tool (LM-TT), an open-source interactive toolkit designed for dissecting Transformer-based language models.
A collaborative effort involving researchers from 33 institutions presents AURORA-M, the inaugural open-source model not only excels in multilingual understanding and coding tasks but also underscores the collaborative ethos of the open-source community, promoting transparency and accessibility in AI development.
In a new paper Stealing Part of a Production Language Model, a research team introduces the first model-stealing attack that unveils precise, nontrivial information from black-box production language models such as OpenAI’s ChatGPT or Google’s PaLM-2.
In a new paper VideoAgent: Long-form Video Understanding with Large Language Model as Agent, a Stanford University research team introduces VideoAgent, an innovative approach simulates human comprehension of long-form videos through an agent-based system, showcasing superior effectiveness and efficiency compared to current state-of-the-art methods.
Google introduces Gemma, a suite of lightweight, cutting-edge open models derived from the same research and technology underpinning the powerful Gemini models, which mark a significant leap forward in performance relative to existing open models across academic benchmarks for language comprehension, reasoning, and safety.
In a new paper BasedAI: A decentralized P2P network for Zero Knowledge Large Language Models (ZK-LLMs), Based Labs proposes BasedAI, which offers a decentralized approach that seamlessly integrates FHE with LLMs to uphold data confidentiality without sacrificing performance.
In a new paper The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits, a research team introduces a new variant of 1-bit LLMs called BitNet b1.58, which preserves the advantages of the original 1-bit BitNet while ushering in a novel computational paradigm that significantly enhances cost-effectiveness in terms of latency, memory usage, throughput, and energy consumption.
In a new paper Nemotron-4 15B Technical Report , an NVIDIA research team introduces Nemotron-4 15B. Nemotron-4 15B is comprising 15 billion parameters, is trained on an extensive corpus of 8 trillion text tokens, showcasing unparalleled multilingual capabilities among models of comparable size.
In a new paper LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens, a Microsoft research team introduces LongRoPE, a pioneering method that extends the context window of pre-trained LLMs to an impressive 2048k tokens while preserving performance at the original short context window.
An Apple research team introduces Keyframer, a groundbreaking animation prototyping tool fueled by LLM technology. Keyframer facilitates the generation of animations from static images (SVGs), empowering users to explore design alternatives, facilitate comparisons, and foster ideation.
In a new paper PokéLLMon: A Human-Parity Agent for Pokémon Battles with Large Language Models, a Georgia Institute of Technology research team introduces PokéLLMon, a pioneering LLM-embodied agent demonstrating human-competent performance in tactical battle games.
In a new paper ChatQA: Building GPT-4 Level Conversational QA Models, an NVIDIA research team introduces ChatQA, a suite of conversational question-answering models that achieve GPT-4 level accuracies without relying on synthetic data from OpenAI GPT models.
In a new breakthrough paper Driving and suppressing the human language network using large language models, a research team from Massachusetts Institute of Technology, MIT-IBM Watson AI Lab, University of Minnesota and Harvard University leverages a GPT-based encoding model to identify sentences predicted to elicit specific responses within the human language network.
In a new paper Towards Conversational Diagnostic AI, a research team from Google Research and Google DeepMind introduces AMIE (Articulate Medical Intelligence Explorer), an LLM-based AI system meticulously optimized for clinical history-taking and diagnostic dialogues, showcasing superior diagnostic accuracy and outperforming primary care physicians (PCPs).
Can a collective of moderately-sized LLMs collaboratively constitute a chat AI with equivalent or superior abilities? Motivated by this query, a new paper “Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM” confirms this idea and introduces the Blended approach.
In a new paper SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling, a Upstage AI research team introduces depth up-scaling (DUS), which emerges as an efficient and uncomplicated technique for amplifying LLMs, surpassing existing open-source state-of-the-art LLMs, such as Llama 2 and Mistral 7B.
In a new paper WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation, a Microsoft research team introduces CodeOcean, which harnesses source code to explicitly control data quality, significantly improving the generalization ability of fine-tuned LLM models.
In a new paper Autonomous chemical research with large language models, a research team from Carnegie Mellon University and Emerald Cloud Lab introduces an innovative LLMs-Powered system named Coscientist, which autonomously designs, plans, and executes complex scientific experiments, marking a significant leap forward in the integration of laboratory automation technologies with powerful language models.
A Google DeepMind research team introduces a groundbreaking family of multimodal models Gemini, which showcase exceptional proficiency across image, audio, video, and text comprehension, pushing the boundaries of large-scale language modeling, image interpretation, audio processing, and video understanding.
In a recent paper titled “Mathematical Discoveries from Program Search with Large Language Models, a research team introduces FunSearch—a novel approach that elevates LLM-guided evolutionary procedures. FunSearch not only achieves breakthroughs in established open problems but also leads to the discovery of new algorithms.
A Microsoft research team introduces TaskWeaver, a cutting-edge, code-first framework designed to empower LLM-powered autonomous agents. TaskWeaver offers a potent and flexible platform for constructing intelligent conversational agents capable of handling complex tasks and seamlessly adapting to domain-specific scenarios.
A collaborative effort between Tencent AI Lab and The University of Sydney introduces GPT4Video, which stands as a unified multi-model framework that endows Large Language Models (LLMs) with the unique ability for both video understanding and generation.
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.
In a new paper An Embodied Generalist Agent in 3D World, a research team introduces LEO, which stands as an embodied multi-modal and multi-task generalist agent that excels in essential capabilities such as perception, grounding, reasoning, planning, and action within the intricate 3D world.
In a new paper Exponentially Faster Language Modelling, an ETH Zurich research team introduces UltraFastBERT, a variant of the BERT architecture. UltraFastBERT takes a revolutionary approach by replacing feedforward layers with fast feedforward networks, resulting in an impressive 78x speedup over the optimized baseline feedforward implementation.
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.
In a new paper Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time, a research team presents DEJAVU, a system that employs a cost-effective algorithm to predict contextual sparsity dynamically for each layer, combined with an asynchronous and hardware-aware implementation to accelerate LLM inference.
A research team from Institute of Science and Technology Austria (ISTA) and Neural Magic Inc. introduces the QMoE framework. This innovative framework offers an effective solution for accurately compressing massive MoEs and conducting swift compressed inference, reducing model sizes by 10–20×, achieving less than 1 bit per parameter.
In a new paper SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF, an NVIDIA research team introduces STEERLM, a novel supervised fine-tuning method that empowers end-users to control model responses during inference, surpassing even state-of-the-art baselines, including RLHF models like ChatGPT-3.5.
A Microsoft research team conducts an in-depth analysis of the latest model, GPT-4V(ision). Their report delves into the emerging application scenarios and outlines future research directions for GPT-4V-based systems, with the goal of inspiring research on next-generation multimodal task formulation and the development of more robust LLMs.
In a recent paper, “CodePlan: Repository-level Coding using LLMs and Planning,” a team from Microsoft Research introduces CodePlan—a versatile framework designed to address the complexities of repository-level coding tasks, encompassing extensive code changes across large, interconnected codebases.
In a new paper Effective Long-Context Scaling of Foundation Models, a Meta AI research team presents a series of long-context LLMs, built through the pretraining from LLAMA 2. These models support effective context windows of up to 32,768 tokens and outperform all existing open-sourced models in terms of performance.
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).
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.
A Microsoft research team introduce phi-1.5, a 1.3 billion parameter model trained on a vast dataset of 30 billion tokens, remarkably delivering performance that rivals models five times its size. Moreover, it outperforms most non-frontier LLMs in tackling intricate reasoning tasks.
In a new paper Large Language Models as Optimizers, a Google DeepMind research team introduces Optimization by PROmpting (OPRO), an effective method that leverages large language models (LLMs) as optimizers, which can generate optimization solutions conditioned on the natural language that describes the optimization task.