Arcee AI launched SuperNova today, a 70 billion parameter language model designed for enterprise deployment, featuring advanced instruction-following capabilities and full customization options. The model aims to provide a powerful, ownable alternative to API-based services from OpenAI and Anthropic, addressing key concerns around data privacy, model stability and customization.
In an AI landscape dominated by cloud-based APIs, Arcee AI is taking a different approach with SuperNova. The large language model (LLM) can be deployed and customized within an enterprise’s own infrastructure. Released today, SuperNova is built on Meta’s Llama-3.1-70B-Instruct architecture and employs a novel post-training process that Arcee claims results in superior instruction adherence and adaptability to specific business needs. — Read More
Monthly Archives: September 2024
LLaMA-Omni: Seamless Speech Interaction with Large Language Models
Models like GPT-4o enable real-time interaction with large language models (LLMs) through speech, significantly enhancing user experience compared to traditional text-based interaction. However, there is still a lack of exploration on how to build speech interaction models based on open-source LLMs. To address this, we propose LLaMA-Omni, a novel model architecture designed for low-latency and high-quality speech interaction with LLMs. LLaMA-Omni integrates a pretrained speech encoder, a speech adaptor, an LLM, and a streaming speech decoder. It eliminates the need for speech transcription, and can simultaneously generate text and speech responses directly from speech instructions with extremely low latency. We build our model based on the latest Llama-3.1-8B-Instruct model. To align the model with speech interaction scenarios, we construct a dataset named InstructS2S-200K, which includes 200K speech instructions and corresponding speech responses. Experimental results show that compared to previous speech-language models, LLaMA-Omni provides better responses in both content and style, with a response latency as low as 226ms. Additionally, training LLaMA-Omni takes less than 3 days on just 4 GPUs, paving the way for the efficient development of speech-language models in the future. — Read More
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning
Instruction tuning is critical to large language models (LLMs) for achieving better instruction following and task adaptation capabilities but its success heavily relies on the training data quality. Many recent methods focus on improving the data quality but often overlook the compatibility of the data with the student model being finetuned. This paper introduces Selective Reflection-Tuning, a novel paradigm that synergizes a teacher LLM’s reflection and introspection for improving existing data quality with the data selection capability of the student LLM, to automatically refine existing instruction-tuning data. This teacher-student collaboration produces high-quality and student-compatible instruction-response pairs, resulting in sample-efficient instruction tuning and LLMs of superior performance. Selective Reflection-Tuning is a data augmentation and synthesis that generally improves LLM finetuning and self-improvement without collecting brand-new data. We apply our method to Alpaca and WizardLM data and achieve much stronger and top-tier 7B and 13B LLMs. — Read More
Reflection 70B model maker breaks silence amid fraud accusations
Matt Shumer, co-founder and CEO of OthersideAI, also known as its signature AI assistant writing product HyperWrite, has broken his near two days of silence after being accused of fraud when third-party researchers were unable to replicate the supposed top performance of a new large language model (LLM) he released on Thursday, September 5.
On his account on the social network X, Shumer apologized and claimed he “Got ahead of himself,” adding “I know that many of you are excited about the potential for this and are now skeptical.” — Read More
SEAL: Systematic Error Analysis for Value ALignment
Reinforcement Learning from Human Feedback (RLHF) aims to align language models (LMs) with human values by training reward models (RMs) on binary preferences and using these RMs to fine-tune the base LMs. Despite its importance, the internal mechanisms of RLHF remain poorly understood. This paper introduces new metrics to evaluate the effectiveness of modeling and aligning human values, namely feature imprint, alignment resistance and alignment robustness. We categorize alignment datasets into target features (desired values) and spoiler features (undesired concepts). By regressing RM scores against these features, we quantify the extent to which RMs reward them – a metric we term feature imprint. We define alignment resistance as the proportion of the preference dataset where RMs fail to match human preferences, and we assess alignment robustness by analyzing RM responses to perturbed inputs. Our experiments, utilizing open-source components like the Anthropic/hh-rlhf preference dataset and OpenAssistant RMs, reveal significant imprints of target features and a notable sensitivity to spoiler features. We observed a 26% incidence of alignment resistance in portions of the dataset where LM-labelers disagreed with human preferences. Furthermore, we find that misalignment often arises from ambiguous entries within the alignment dataset. These findings underscore the importance of scrutinizing both RMs and alignment datasets for a deeper understanding of value alignment. — Read More
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Self-supervised features are the cornerstone of modern machine learning systems. They are typically pre-trained on data collections whose construction and curation typically require extensive human effort. This manual process has some limitations similar to those encountered in supervised learning, e.g., the crowd-sourced selection of data is costly and time-consuming, preventing scaling the dataset size. In this work, we consider the problem of automatic curation of high-quality datasets for self-supervised pre-training. We posit that such datasets should be large, diverse and balanced, and propose a clustering-based approach for building ones satisfying all these criteria. Our method involves successive and hierarchical applications of k-means on a large and diverse data repository to obtain clusters that distribute uniformly among data concepts, followed by a hierarchical, balanced sampling step from these clusters. Extensive experiments on three different data domains including web-based images, satellite images and text show that features trained on our automatically curated datasets outperform those trained on uncurated data while being on par or better than ones trained on manually curated data. Code is available at this https URL. — Read More
SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement
We present SF3D, a novel method for rapid and high-quality textured object mesh reconstruction from a single image in just 0.5 seconds. Unlike most existing approaches, SF3D is explicitly trained for mesh generation, incorporating a fast UV unwrapping technique that enables swift texture generation rather than relying on vertex colors. The method also learns to predict material parameters and normal maps to enhance the visual quality of the reconstructed 3D meshes. Furthermore, SF3D integrates a delighting step to effectively remove low-frequency illumination effects, ensuring that the reconstructed meshes can be easily used in novel illumination conditions. Experiments demonstrate the superior performance of SF3D over the existing techniques. Project page: this https URL. — Read More
Time100/AI
…Our purpose in creating the TIME100 AI is to put leaders like [Sundar] Pichai and [Meredith] Whittaker in dialogue and to open up their views to TIME’s readers. That is why we are excited to share with you the second edition of the TIME100 AI. We built this program in the spirit of the TIME100, the world’s most influential community. TIME’s knowledgeable editors and correspondents, led by Emma Barker and Ayesha Javed, interviewed their sources and consulted members of last year’s list to find the best new additions to our community of AI leaders. Ninety-one of the members of the 2024 list were not on last year’s, an indication of just how quickly this field is changing. They span dozens of companies, regions, and perspectives, including 15-year-old Francesca Mani, who advocates across the U.S. for protections for victims of deepfakes, and 77-year-old Andrew Yao, one of China’s most prominent computer scientists, who called last fall for an international regulatory body for AI. — Read More
Superhuman Automated Forecasting (FiveThirtyNine)
In a recent appearance on Conversations with Tyler, famed political forecaster Nate Silver expressed skepticism about AIs replacing human forecasters in the near future. When asked how long it might take for AIs to reach superhuman forecasting abilities, Silver replied: “15 or 20 [years].”
In light of this, we are excited to announce “FiveThirtyNine,” a superhuman AI forecasting bot. Our bot, built on GPT-4o, provides probabilities for any user-entered query, including “Will Trump win the 2024 presidential election?” and “Will China invade Taiwan by 2030?” Our bot performs better than experienced human forecasters and performs roughly the same as (and sometimes even better than) crowds of experienced forecasters; since crowds are for the most part superhuman, so is FiveThirtyNine. — Read More
AI worse than humans in every way at summarising information, government trial finds
Artificial intelligence is worse than humans in every way at summarising documents and might actually create additional work for people, a government trial of the technology has found.
Amazon conducted the test earlier this year for Australia’s corporate regulator the Securities and Investments Commission (ASIC) using submissions made to an inquiry. The outcome of the trial was revealed in an answer to a questions on notice at the Senate select committee on adopting artificial intelligence.
… [R]eviewers overwhelmingly found that the human summaries beat out their AI competitors on every criteria and on every submission, scoring an 81% on an internal rubric compared with the machine’s 47%. — Read More
Rick's Cafe AI 