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CREAM: A New Self-Rewarding Method that Allows the Model to Learn more Selectively and Emphasize on Reliable Preference Data
One of the most critical challenges of LLMs is how to align these models with human values and preferences, especially in generated texts. Most generated text outputs by models are inaccurate, biased, or potentially harmful—for example, hallucinations. This misalignment limits the potential usage of LLMs in real-world applications across domains such as education, health, and…
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Self-Data Distilled Fine-Tuning: A Solution for Pruning and Supervised Fine-tuning Challenges in LLMs
Large language models (LLMs) like GPT-4, Gemini, and Llama 3 have revolutionized natural language processing through extensive pre-training and supervised fine-tuning (SFT). However, these models come with high computational costs for training and inference. Structured pruning has emerged as a promising method to improve LLM efficiency by selectively removing less critical components. Despite its potential,…
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Differentiable Rendering of Robots (Dr. Robot): A Robot Self-Model Differentiable from Its Visual Appearance to Its Control Parameters
Visual and action data are interconnected in robotic tasks, forming a perception-action loop. Robots rely on control parameters for movement, while VFMs excel in processing visual data. However, a modality gap exists between visual and action data arising from the fundamental differences in their sensory modalities, abstraction levels, temporal dynamics, contextual dependence, and susceptibility to…
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Harnessing Introspection in AI: How Large Language Models Are Learning to Understand and Predict Their Behavior for Greater Accuracy
Large Language models (LLMs) have long been trained to process vast amounts of data to generate responses that align with patterns seen during training. However, researchers are exploring a more profound concept: introspection, the ability of LLMs to reflect on their behavior and gain knowledge that isn’t directly derived from their training data. This new…
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Rethinking Direct Alignment: Balancing Likelihood and Diversity for Better Model Performance
The problem of over-optimization of likelihood in Direct Alignment Algorithms (DAAs), such as Direct Preference Optimisation (DPO) and Identity Preference Optimisation (IPO), arises when these methods fail to improve model performance despite increasing the likelihood of preferred outcomes. These algorithms, which are alternatives to Reinforcement Learning from Human Feedback (RLHF), aim to align language models…
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Meta AI Releases Cotracker3: A Semi-Supervised Tracker that Produces Better Results with Unlabelled Data and Simple Architecture
Point tracking is paramount in video; from 3d reconstruction to editing tasks, a precise approximation of points is necessary to achieve quality results. Over time, trackers have incorporated transformer and neural network-based designs to track individual and multiple points simultaneously. However, these neural networks could be fully exploited only with high-quality training data. Now, while…
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Embed-then-Regress: A Versatile Machine Learning Approach for Bayesian Optimization Using String-Based In-Context Regression
Bayesian Optimization, widely used in experimental design and black-box optimization, traditionally relies on regression models for predicting the performance of solutions within fixed search spaces. However, many regression methods are task-specific due to modeling assumptions and input constraints. This issue is especially prevalent in learning-based regression, which depends on fixed-length tensor inputs. Recent advancements in…
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Nvidia AI Introduces the Normalized Transformer (nGPT): A Hypersphere-based Transformer Achieving 4-20x Faster Training and Improved Stability for LLMs
The rise of Transformer-based models has significantly advanced the field of natural language processing. However, the training of these models is often computationally intensive, requiring substantial resources and time. This research addresses the issue of improving the training efficiency of Transformer models without compromising their performance. Specifically, it seeks to explore whether the benefits of…
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MMed-RAG: A Versatile Multimodal Retrieval-Augmented Generation System Transforming Factual Accuracy in Medical Vision-Language Models Across Multiple Domains
AI has significantly impacted healthcare, particularly in disease diagnosis and treatment planning. One area gaining attention is the development of Medical Large Vision-Language Models (Med-LVLMs), which combine visual and textual data for advanced diagnostic tools. These models have shown great potential for improving the analysis of complex medical images, offering interactive and intelligent responses that…
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14 Gifts for People Who Are Perpetually Cold (2024)
From tiny mugs to a backyard hot tub, these picks will make your loved ones feel warm and fuzzy—inside and out.