Papers Read on AI

About 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.

Publisher

Rob

Category

news

Language

en

Explicit

No

First episode

8 May 2023

Latest episode

1 Nov 2024

Latest episodes

174 of 333 episodes have a transcript.

1. 

   1 Nov 2024TRANSCRIPT

   ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases

   Enabling large language models to utilize real-world tools effectively is crucial for achieving embodied intelligence. Existing approaches to tool learning have either primarily relied on extremely large language models, such as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or utilized supervised learning to train limited scopes of tools on compact models. However, it remains uncertain whether smaller language models can achieve generalized tool-use abilities without tool-specific training. To address this question, this paper introduces ToolAlpaca, a novel framework designed to automatically generate a diverse tool-use corpus and learn generalized tool-use abilities on compact language models with minimal human intervention. Specifically, ToolAlpaca first automatically creates a highly diversified tool-use corpus by building a multi-agent simulation environment. The corpus contains 3938 tool-use instances from more than 400 real-world tool APIs spanning 50 distinct categories. Subsequently, the constructed corpus is employed to fine-tune compact language models, resulting in two models, namely ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the ability of these models to utilize previously unseen tools without specific training. Experimental results demonstrate that ToolAlpaca achieves effective generalized tool-use capabilities comparable to those of extremely large language models like GPT-3.5, demonstrating that learning generalized tool-use ability is feasible for compact language models.2023: Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sunhttps://arxiv.org/pdf/2306.05301

2. 

   31 Oct 2024TRANSCRIPT

   Mini-Omni2: Towards Open-source GPT-4o with Vision, Speech and Duplex Capabilities

   GPT-4o, an all-encompassing model, represents a milestone in the development of large multi-modal language models. It can understand visual, auditory, and textual modalities, directly output audio, and support flexible duplex interaction. Models from the open-source community often achieve some functionalities of GPT-4o, such as visual understanding and voice chat. Nevertheless, training a unified model that incorporates all modalities is challenging due to the complexities of multi-modal data, intricate model architectures, and training processes. In this paper, we introduce Mini-Omni2, a visual-audio assistant capable of providing real-time, end-to-end voice responses to visoin and audio queries. By integrating pretrained visual and auditory encoders, Mini-Omni2 maintains performance in individual modalities. We propose a three-stage training process to align modalities, allowing the language model to handle multi-modal inputs and outputs after training on a limited dataset. For interaction, we introduce a command-based interruption mechanism, enabling more flexible interaction with users. To the best of our knowledge, Mini-Omni2 is one of the closest reproductions of GPT-4o, which have similar form of functionality, and we hope it can offer valuable insights for subsequent research.2024: Zhifei Xie, Changqiao Wuhttps://arxiv.org/pdf/2410.11190

3. 

   30 Oct 2024TRANSCRIPT

   Hallo2: Long-Duration and High-Resolution Audio-Driven Portrait Image Animation

   Recent advances in latent diffusion-based generative models for portrait image animation, such as Hallo, have achieved impressive results in short-duration video synthesis. In this paper, we present updates to Hallo, introducing several design enhancements to extend its capabilities. First, we extend the method to produce long-duration videos. To address substantial challenges such as appearance drift and temporal artifacts, we investigate augmentation strategies within the image space of conditional motion frames. Specifically, we introduce a patch-drop technique augmented with Gaussian noise to enhance visual consistency and temporal coherence over long duration. Second, we achieve 4K resolution portrait video generation. To accomplish this, we implement vector quantization of latent codes and apply temporal alignment techniques to maintain coherence across the temporal dimension. By integrating a high-quality decoder, we realize visual synthesis at 4K resolution. Third, we incorporate adjustable semantic textual labels for portrait expressions as conditional inputs. This extends beyond traditional audio cues to improve controllability and increase the diversity of the generated content. To the best of our knowledge, Hallo2, proposed in this paper, is the first method to achieve 4K resolution and generate hour-long, audio-driven portrait image animations enhanced with textual prompts. We have conducted extensive experiments to evaluate our method on publicly available datasets, including HDTF, CelebV, and our introduced"Wild"dataset. The experimental results demonstrate that our approach achieves state-of-the-art performance in long-duration portrait video animation, successfully generating rich and controllable content at 4K resolution for duration extending up to tens of minutes. Project page https://fudan-generative-vision.github.io/hallo22024: Jiahao Cui, Hui Li, Yao Yao, Hao Zhu, Hanlin Shang, Kaihui Cheng, Hang Zhou, Siyu Zhu, Jingdong Wanghttps://arxiv.org/pdf/2410.07718

Inside a recent episode

ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases

Published 1 Nov 2024 · Transcript excerpt

[…] Consequently, we have crafted an inclusive tool use dataset that comprises 3938 instances, effectively showcasing the practical application of over 400 distinct tools. To verify whether our corpus can empower compact language models with the generalized tool usability, we conduct experiments to train tool alpaca model on Vakunya, Chang -Hedall. 2023, a representative compact language model, and subsequently evaluate its performance on various unseen tools. Through machine evaluation with GPT -4, we find that tool alpaca can effectively equip numerous unseen tools, ranging from real -world APIs to multimodal tools, and it exhibits competitive performance with GPT -3 .5. […]

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Apple Podcasts (US)

3.7 / 5

3 ratings

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3.2 / 5

6 ratings

Podcast Authority Score: 6 / 100

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