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    Papers Read on AI

    Papers Read on AI

    Keeping you up to date with the latest trends and best performing architectures in this fast evolving field in computer science. Selecting papers by comparative results, citations and influence we educate you on the latest research. Consider supporting us on Patreon.com/PapersRead for feedback and ideas.

    Papers Read on AI

    Fri Jul 26 2024

    Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?

    multimodal agentsdata scienceengineering workflowsbenchmarkSpider2V

    Spider2V is a benchmark for multimodal agents aiming to automate data science and engineering workflows. It includes 494 real-world tasks in authentic computer environments and 20 professional applications. Existing state-of-the-art large-language models struggle to reliably automate full data workflows, achieving only around 14% success rate. Multimodal agents face challenges in tasks requiring fine-grained GUI actions and remote cloud-hosted workspaces. Spider2V provides a realistic simulation environment for evaluating multimodal agents' performance in executing data-related tasks. The benchmark aims to bridge the gap in automating entire data workflows by integrating code generation and GUI controls.

    Papers Read on AI

    Thu Jul 25 2024

    FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs

    voice interactionsspeech recognitionvoice generationmultilingualemotion recognition

    FunAudio LLM introduces innovative models, SenseVoice and CozyVoice, for enhancing natural voice interactions between humans and large language models. SenseVoice offers multilingual speech recognition, emotion recognition, and audio event detection with low-latency OSR for multiple languages. CozyVoice excels in multilingual voice generation, zero-shot learning, cross-lingual voice cloning, and instruction-following capabilities. The models related to SenseVoice and CozyVoice have been open-sourced on ModelScope and HuggingFace. FunAudio LLM enables applications like speech-to-speech translation, emotional voice chat, interactive podcasts, and expressive audiobook narration by integrating these models with LLMs.

    Papers Read on AI

    Wed Jul 24 2024

    Patch-Level Training for Large Language Models

    language modelstraining efficiencypatch-level training

    This episode discusses patch-level training for large language models (LLMs) to improve training efficiency. LLMs have achieved remarkable progress in language understanding and generation, but scaling them up comes with a substantial rise in computational costs. Patch-level training reduces the sequence length by compressing multiple tokens into a single patch, significantly reducing computational costs without compromising model performance. The training process is divided into two stages: patch-level training and token-level training. Extensive experiments show that patch-level training can achieve similar performance to token-level training with reduced computational resources. The optimal values for hyperparameters like patch size and lambda are determined through experimentation.

    Papers Read on AI

    Tue Jul 23 2024

    Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

    WikipediaArticle WritingSTORMLanguage Models

    Large language models can automate the pre-writing stage of creating long-form articles like Wikipedia pages. STORM is a writing system that assists in this process by researching topics, creating outlines, and curating information. It outperforms other baselines in outline coverage and article quality. However, there are limitations and ethical considerations to be addressed.

    Papers Read on AI

    Mon Jul 22 2024

    IMAGDressing-v1: Customizable Virtual Dressing

    virtual dressingvirtual try-onIMAG Dressing V1diffusion modelsIGPair dataset

    The episode discusses the latest advances in virtual dressing technology, including IMAG Dressing V1, which offers custom virtual dressing for merchants. It explores the use of pre-trained diffusion models for virtual try-on tasks and the limitations of diffusion-based methods. The IGPair dataset is introduced as a valuable resource for virtual dressing datasets. The episode also covers the anonymization of model images and the proposal of a comprehensive affinity metric index. Finally, it compares IMAG Dressing V1 with state-of-the-art methods and provides training details.

    Papers Read on AI

    Fri Jul 19 2024

    A Comprehensive Survey on Human Video Generation: Challenges, Methods, and Insights

    human video generationgenerative modelsevaluation metricspose-driven motionadvancements in video generation

    Human video generation is a dynamic task that aims to synthesize 2D human body video sequences using generative models with control conditions like text, audio, and pose. This survey provides a comprehensive review of current methods and insights in human video generation, categorizing advancements into text-driven, audio-driven, and pose-driven motion generation. The survey offers valuable insights into enhancing current methodologies and suggests promising directions for future research in human motion generation. Evaluation metrics for generated human videos cover aspects like image quality, structural similarity, visual similarity, and feature distribution comparison.

    Papers Read on AI

    Thu Jul 18 2024

    Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence

    multi-agent systemscollaborationagent integrationcommunicationtask execution

    The Internet of Agents (IOA) is a novel framework designed to enhance collaboration among diverse autonomous agents. It addresses limitations in existing multi-agent frameworks by providing flexibility and scalability for agent collaboration. IOA enables distributed agent collaboration across multiple devices, dynamic communication strategies, and integration of diverse third-party agents. The framework consists of server and client components with layered architectures to manage agent registration, discovery, communication, and task execution.

    Papers Read on AI

    Tue Jul 16 2024

    SEED-Story: Multimodal Long Story Generation with Large Language Model

    multimodal story generationSeedStoryMLLMStoryStream dataset

    This episode discusses SeedStory, a novel method for multimodal story generation using a multimodal large-language model. The method leverages the model's comprehension capability to generate extended stories with narrative texts and vivid images. The model is trained using the StoryStream dataset, a large-scale and high-resolution dataset. Experimental results show that SeedStory outperforms other models in various aspects.

    Papers Read on AI

    Mon Jul 15 2024

    Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

    large-language modelsLATS frameworkMonte Carlo tree searchdecision-makingreasoning abilities

    Large-language models (LLMs) have limitations in acting processes, leading to the proposal of LATS, a framework that combines planning, acting, and reasoning abilities. LATS utilizes Monte Carlo tree search to enhance decision-making by repurposing LLMs' strengths for diverse domains like programming and web browsing. Existing methods augmenting LLMs with external feedback fall short of human-like deliberate decision-making characteristics, prompting the development of LATS for autonomous reasoning. LATS outperforms previous models on tasks like Hotpot QA and webshop navigation by doubling performance and raising average scores significantly. The framework combines reasoning, acting, and planning to adaptively solve problems using heuristics from LLMs while integrating external feedback for enhanced model sensibility.

    Papers Read on AI

    Fri Jul 12 2024

    LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control

    portrait animationvideo-driven frameworkgeneration qualitymotion accuracyethical considerations

    The episode discusses Live Portrait, a new video-driven portrait animation framework that creates lifelike videos from single source images. The framework focuses on enhancing generation quality and generalization ability through various techniques. Different methods for portrait animation are explored, along with enhancements made to existing frameworks. The evaluation and comparison of the model show its superiority in terms of generation quality and motion accuracy. The limitations and ethical considerations of portrait animation technologies are also discussed. The episode references various research papers on 3D control over portrait images.

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