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This AI Paper from CMU and Meta AI Unveils Pre-Instruction-Tuning (PIT): A Game-Changer for Training Language Models on Factual Knowledge

In the fast-paced world of artificial intelligence, the challenge of keeping large language models (LLMs) up-to-date with the latest factual knowledge is paramount. These models, which have become the backbone of numerous AI applications, store a wealth of information during their initial training phase. However, as time passes, the static nature of this stored knowledge becomes a limitation, unable to accommodate the constant evolution of real-world information or specialize in niche domains.

Recent studies have highlighted a promising approach to this problem: instruction-tuning. This method enhances the ability of LLMs to access and update their knowledge base more effectively. By continuing the pre-training process with new documents and applying instruction-tuning techniques, researchers have found significant improvements in the models’ performance. Specifically, experiments with models like Llama-2 have shown that this ongoing training can increase the accuracy of answers to specific questions by up to 30.3%, compared to 27.6% without instruction tuning. This process, however, uncovers the “perplexity curse,” where despite achieving low perplexity (a measure of prediction accuracy), the models still face limits in extracting knowledge effectively from new documents.

Reference: https://arxiv.org/pdf/2402.12847.pdf

To address these challenges, researchers propose pre-instruction-tuning (PIT), which prioritizes exposing LLMs to question-answer (QA) pairs before engaging with more complex document materials as shown in Figure 1 and 4. This strategy is grounded in the hypothesis that understanding how to access knowledge through questions enhances the model’s ability to assimilate and retain new information from detailed documents. The Wiki2023 dataset, comprising up-to-date Wikipedia articles, serves as a testbed for these experiments, revealing that models trained with a combination of QA pairs and documents exhibit superior knowledge absorption capabilities.

Quantitative results underscore the superiority of PIT over traditional instruction-tuning methods: PIT has led to a significant increase in QA accuracies, with a 17.8% improvement for Llama-2 7B models (from 30.3% to 48.1%) and a 16.3% boost for Llama-2 70B models (from 46.4% to 62.7%). Moreover, this method ensures that models not only memorize information but also truly comprehend its application, improving their ability to answer questions accurately. The introduction of pre-instruction-tuning++ (PIT++), which further refines the training process by focusing on the sequence of QA and document exposure, marks a significant leap forward. This method significantly enhances the model’s performance, confirming the importance of strategic training sequences in knowledge acquisition.

Overall, the research presents a compelling case for the benefits of continued pre-training and instruction-tuning in enhancing LLMs’ ability to stay current with evolving knowledge. By adopting these advanced training methodologies, models like Llama-2 show improved performance in answering questions accurately and promise greater adaptability across various domains. As we move forward, the potential to expand these techniques to encompass a broader spectrum of documents and instructions opens new avenues for achieving more resilient and versatile AI systems. Yet, the journey doesn’t end here; the exploration of these methods’ applicability to other skills like reasoning and comprehension, as well as their effectiveness across different data types, remains a vital area for future research.

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Enhancing AI’s Foresight: The Crucial Role of Discriminator Accuracy in Advanced LLM Planning Methods

The ability of systems to plan and execute complex tasks stands as a testament to AI’s progress. Panning within AI has been approached through various methodologies, ranging from basic decision-making processes to complex algorithms designed to simulate the foresight and adaptability of human intelligence. As the intricacy of problems addressed by AI systems has escalated, so too has the necessity for innovative planning strategies that can navigate these challenges with greater precision and efficiency.

Large language models (LLMs), which have shown remarkable capabilities in generating human-like text, can be leveraged for multi-step problem-solving. Central to this exploration is the concept of a language agent framework that incorporates a generator for creating potential solutions, a discriminator for evaluating these solutions, and a planning method to select the most promising path forward. This framework represents a significant shift from traditional AI planning methods, emphasizing the role of discrimination accuracy in the effectiveness of planning strategies.

The researchers from Ohio State University, The University of Texas at Austin, and Cisco Research focus particularly on the comparison between two advanced planning methods, iterative correction and tree search, against a simpler baseline method known as re-ranking. Iterative correction involves refining initial solutions based on feedback, while tree search explores a wider range of potential solutions before selecting the best one. Both methods promise improved outcomes by leveraging the nuanced understanding of LLMs, but their success hinges on the discriminator’s ability to accurately assess the viability of proposed solutions.

Through rigorous experimentation on tasks such as text-to-SQL parsing and mathematical reasoning, the study sheds light on the critical role of discriminator accuracy. It emerges that for advanced planning methods to surpass the performance of simpler strategies, the discriminator must achieve a high level of accuracy. At least 90% accuracy is required to realize significant improvements over re-ranking. This finding underscores the gap between the current capabilities of LLM-based discriminators and the demands of more sophisticated planning methods.

The research reveals that while advanced planning methods like tree search offer the allure of more comprehensive solution exploration, they also introduce significant challenges in terms of efficiency. The extensive computational resources and time required by tree search, for instance, often translate to negligible gains in performance when compared to simpler methods. This discrepancy raises questions about the practical applicability of such advanced planning strategies in real-world scenarios, where efficiency and speed are of paramount importance.

The study also contributes to the broader discourse on the evolution of AI problem-solving strategies. By highlighting the pivotal role of discriminator accuracy in the effectiveness of advanced planning methods, the research points to a critical area for future development. Enhancing the accuracy and efficiency of discriminators could unlock the full potential of sophisticated planning strategies, enabling AI systems to tackle more complex problems with unprecedented proficiency.

In conclusion, the investigation into the utility of tree search and other advanced planning methods in the context of LLM planning represents a significant step forward in our understanding of AI’s problem-solving capabilities. It reveals the intricate balance between the sophistication of planning strategies and the accuracy of discriminators, offering insights that could guide the future development of more intelligent and efficient AI systems. 

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This Artificial Intelligence (AI) Stock Is About to Go on a Bull Run – The Motley Fool

This post was originally published on this site The PHLX Semiconductor Sector index is off to a strong start in 2024 with gains of 11% so far. That’s not surprising as some of its key components such as Nvidia (NVDA 4.00%), AMD, Broadcom, and Taiwan Semiconductor Manufacturing have already jumped nicely thanks to their solid

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2 Unstoppable Artificial Intelligence (AI) Stocks Up More Than 200% to Buy and Hold for the Long Term – Yahoo Finance

This post was originally published on this siteArtificial intelligence (AI) has grabbed investors’ attention thanks to its potential to revolutionize so many industries, from healthcare to automotive. In fact, some researchers predict the size of the AI market could soar past $1 trillion by 2030. So, investors have bought shares of possible winners, including those

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Prediction: This Artificial Intelligence (AI) Stock Could Be the Next Nvidia – The Motley Fool

This post was originally published on this site Finding the next great stock, like an Amazon or, more recently, Nvidia (NVDA 4.00%), is the dream — one stock that can generate portfolio- and life-changing returns, sometimes very quickly. Super Micro Computer (SMCI 4.54%) has given investors its best Nvidia impression. The stock is up a

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Google AI Introduces VideoPrism: A General-Purpose Video Encoder that Tackles Diverse Video Understanding Tasks with a Single Frozen Model

Google researchers address the challenges of achieving a comprehensive understanding of diverse video content by introducing a novel encoder model, VideoPrism. Existing models in video understanding have struggled with various tasks with complex systems and motion-centric reasoning and demonstrated poor performance across different benchmarks. The researchers aimed to develop a general-purpose video encoder that can effectively tackle a wide range of video understanding tasks with minimal adaptation.

Existing video understanding models have made significant progress but still fall short of. Some models leverage text associated with videos for learning, and others focus solely on video signals, which limits the effective capture of both appearance and motion cues. VideoPrism proposes an approach that integrates both video and text modalities during pretraining. It introduces a two-stage pretraining framework that combines contrastive learning with masked video modeling. This method enables the model to learn semantic representations from both video-text pairs and video-only data.

VideoPrism’s architecture is based on the Vision Transformer (ViT) with modifications for space-time factorization. During pretraining, the model first aligns video and text embeddings through contrastive learning and then continues training on video-only data using masked video modeling. This two-stage approach is augmented with global-local distillation and token shuffling techniques to improve model performance. Extensive evaluations across various video understanding tasks demonstrate that VideoPrism achieves state-of-the-art performance on 30 out of 33 benchmarks, showcasing its robust generalizability and effectiveness in capturing both appearance and motion cues.

Google researchers address the challenge of building a foundational video model with their state-of-the-art model VideoPrism for comprehensive video understanding. The proposed method combines contrastive learning with masked video modeling in a two-stage pretraining framework, resulting in a model that excels across a wide range of video understanding tasks.

Check out the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and Google News. Join our 38k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and LinkedIn Group.

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Harmonizing Vision and Language: Advancing Consistency in Unified Models with CocoCon

Unified vision-language models have emerged as a frontier, blending the visual with the verbal to create models that can interpret images and respond in human language. However, a stumbling block in their development has been ensuring that these models behave consistently across different tasks. The crux of the problem lies in the model’s ability to produce coherent and reliable outputs, whether they are identifying objects in images, answering questions based on those images, or generating textual descriptions from visual inputs.

Recent advancements have propelled these models to impressive heights, enabling them to tackle a wide array of multimodal tasks. Yet, this versatility has unveiled a critical issue: inconsistent responses across different tasks. Such inconsistencies erode trust in these models, making their integration into practical applications challenging. Imagine a model that identifies two jaguars in an image but contradicts itself when asked to describe the same scene in text. This inconsistency needs to be clarified for users and also undermines the model’s reliability.

Researchers from the University of North Carolina, the University of California Los Angeles, and the Allen Institute for AI have developed a benchmark dataset, CocoCon, designed to evaluate and enhance the consistency of these models across various tasks. By creating contrast sets and modifying test instances in small but meaningful ways, the researchers can assess if a model’s responses remain consistent when the input changes slightly. This methodology revealed a significant degree of inconsistency among state-of-the-art vision-language models, particularly when tasks varied widely in their output format.

The study introduces a novel training objective based on rank correlation. This objective encourages models to maintain a consistent ranking of potential responses across tasks, thereby aligning their understanding of an image regardless of the question or task at hand. Preliminary results indicate that this approach not only improves cross-task consistency but also preserves, or even enhances, the model’s original accuracy on specific tasks.

This research underscores the importance of consistency in the development of unified vision-language models. By demonstrating the prevalence of cross-task inconsistency and proposing a method to mitigate it, the study paves the way for more reliable and trustworthy AI systems. The CocoCon benchmark emerges as a valuable tool in this endeavor, offering a means to rigorously evaluate and refine these complex models.

In conclusion, the implications of this work extend far beyond academic curiosity. In a world increasingly reliant on AI, the ability to trust the outputs of vision-language models becomes paramount. Whether for accessibility purposes, content creation, or even autonomous vehicles, the consistency ensured by approaches like those proposed in this study will be critical in realizing the full potential of AI in our daily lives. The journey toward models that can see and speak as we do, with all the nuance and reliability expected of human interaction, is just beginning.

Check out the Paper and Github. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and Google News. Join our 38k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and LinkedIn Group.

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GenAI: The User Interface to Artificial Intelligence (AI)? – DataScienceCentral.com – Data Science Central

This post was originally published on this site Generative AI (GenAI) Chatbots like Microsoft Copilot (formerly Bing AI), Google’s Gemini (formerly Google Bard), and OpenAI ChatGPT (still OpenAI ChatGPT) are driving extraordinary productivity improvements by assisting knowledge workers in providing highly relevant information, answering questions, and engaging in wide-ranging exploratory conversations. However, the Wall Street

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3 Artificial Intelligence (AI) Stocks That Could Be Worth $1 Trillion Someday – Yahoo Finance

This post was originally published on this site Artificial intelligence (AI) is changing the world. It goes far beyond OpenAI’s ChatGPT and other chatbots. AI will personalize products and ads to customers. It will enable humans to work smarter and faster and may even perform certain tasks so people don’t have to. According to a

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Meet Phind-70B: An Artificial Intelligence (AI) Model that Closes Execution Speed and the Code Generation Quality Gap with GPT-4 Turbo

The field of Artificial Intelligence (AI) is significantly pushing the envelope of technology, thanks to the amazing capabilities of Large Language Models (LLMs). These models based on Natural Language Processing, Understanding, and Generation have demonstrated exceptional skills and potential in almost every industry.  

In recent research, a new development has emerged that can greatly improve the coding experiences of developers across the globe. A team of researchers has released Phind-70B, a state-of-the-art AI model with the goal of closing the execution speed and code quality gap with respect to its predecessors, including the well-known GPT-4 Turbo.

Phind-70B  has been built upon the CodeLlama-70B model as a basis and has undergone considerable refinement with 50 billion extra tokens. After a thorough development process, the team has shared that the model can provide excellent answers on technical topics while operating at an unparalleled pace of up to 80 tokens per second. With this development, coders can get instant feedback.

Beyond its speed, the Phind-70B can generate complicated code sequences and understand deeper contexts with the help of its 32K token context window. This characteristic greatly enhances the model’s capacity to offer thorough and pertinent coding solutions. When it comes to performance measures, Phind-70B has shown impressive results. 

The team has shared that in the HumanEval benchmark, Phind-70B has shown better performance than GPT-4 Turbo, achieving 82.3% as opposed to 81.1% for GPT-4 Turbo. On Meta’s CRUXEval dataset, it scored 59% compared to 62%, which is a tiny loss behind GPT-4 Turbo, but it’s crucial to remember that these benchmarks do not really reflect the model’s effectiveness in practical applications. Phind-70B excels in real-world workloads, demonstrating exceptional code generation skills and a willingness to produce thorough code samples without reluctance.

Phind-70B’s amazing performance is mostly due to its speed, which is four times faster than the GPT-4 Turbo. The team has shared that Phind-70B has utilized the TensorRT-LLM library from NVIDIA on the newest H100 GPUs, which allowed for a significant increase in efficiency and improvement in the model’s inference performance.

The team has partnered with cloud partners SF Compute and AWS, which ensured the best infrastructure for training and deploying Phind-70B. To enable more people to have access to the product, Phind-70B has offered a free trial that doesn’t require a login. A Phind Pro subscription has been offered for those looking for even more features and limits, providing an even more comprehensive coding aid experience.

The Phind-70B development team has shared that the weights for the Phind-34B model will soon be made public, and there are plans to eventually publish the weights of the Phind-70B model as well, further fostering a culture of cooperation and creativity.

In conclusion, Phind-70B is a great example of innovation, promising to improve the developer experience with a combination of unrivaled speed and code quality. In terms of improving the effectiveness, accessibility, and impact of AI-assisted coding, Phind-70B is a big step forward.

Check out the Blog and Tool. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and Google News. Join our 38k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and LinkedIn Group.

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