Multimodal LLMs; Orca 2; Cosmopedia – Largest Open Synthetic Data by Huggin Face; How To Fine-Tune On Single GPU; and More.
Danny Butvinik
Chief Data Scientist | 100K+ Followers | FinCrime | Writer | Author of AI Vanguard Newsletter
Editor's Paper Recommendations
Multimodal Large Language Models : A Survey: The exploration of multimodal language models integrates multiple data types, such as images, text, language, audio, and other heterogeneity. While the latest large language models excel in text-based tasks, they often need help understanding and processing
different data types. Multimodal models address this limitation by combining various modalities, enabling a more comprehensive understanding of diverse data. This paper begins by defining the concept of multimodality and examining the historical development of multimodal algorithms. Furthermore, we introduce a range of multimodal products, focusing on the efforts of major technology companies. A practical guide is provided, offering insights into the technical aspects of multimodal models. Moreover, we present a compilation of the latest algorithms and commonly used datasets, providing researchers with valuable resources for experimentation and evaluation. Lastly, we explore the applications of multimodal models and discuss the challenges associated with their development. This paper aims to facilitate a deeper understanding of multimodal models and their potential in various domains by addressing these aspects.
Classification of Tabular Data by Text Processing: Natural Language Processing technology has advanced vastly in the past decade. Text processing has been successfully applied to a wide variety of domains. This paper proposes a novel Text Based on Classification (TBC) framework that uses state-of-the-art text processing techniques to solve classification tasks on tabular data. We provide a set of controlled experiments where we present the benefits of using this approach against other classification methods. Experimental results on several data sets also show that this framework performs comparably to several state-of-the-art models' accuracy, precision, and recall of predicted classes.
Orca 2: Teaching Small Language Models How to Reason : Orca 1 learns from rich signals, such as explanation traces, to outperform conventional instruction-tuned models on benchmarks like BigBench Hard and AGIEval. In Orca 2, we continue exploring how improved training signals can enhance smaller LMs' reasoning abilities. Research on training small LMs has often relied on imitation learning to replicate the output of more capable models. Excessive emphasis on imitation may restrict the potential of smaller models. We seek to teach small LMs to employ different solution strategies for various tasks, potentially different from the larger models. For example, while larger models might directly answer a complex task, smaller models may have different capacities. In Orca 2, we teach the model various reasoning techniques (step-by-step, recall then generate, recall-reason-generate, direct answer, etc.). More crucially, we aim to help the model learn to determine the most effective solution strategy for each task. We evaluate Orca 2 using a comprehensive set of 15 diverse benchmarks (corresponding to approximately 100 tasks and over 36,000 unique prompts). Orca 2 significantly surpasses models of similar size and attains performance levels similar to or better than those of models 5–10 times larger, as assessed on complex tasks that test advanced reasoning abilities in zero-shot settings. Make Orca 2 weights publicly available at this http URL  to support research on developing, evaluating, and aligning smaller LMs.
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8moYou mentioned a diverse array of topics in the AI Vanguard Newsletter, covering everything from multimodal LLMs to the unveiling of Cosmopedia by Hugging Face. Reflecting on the historical trajectory of synthetic data, initiatives like Cosmopedia showcase a pivotal shift toward creating expansive and open datasets. Drawing parallels with the impact of diverse teams, historical data indicates that varied perspectives lead to innovative breakthroughs. Now, considering the How To Fine-Tune On Single GPU topic, I'm curious about your perspective on the scalability challenges and efficiency trade-offs associated with such approaches, especially when applied to large-scale models like multimodal LLMs. How do you envision overcoming bottlenecks in single GPU fine-tuning for optimal results in your specific applications?