​[[{“value”:” A primary feature of sophisticated language models is In-Context Learning (ICL), which allows the model to produce answers based on input instances without being specifically instructed on how to complete the task. In ICL, a few examples that show the intended behavior or pattern… Read More »This Machine Learning Research Discusses How Task Diversity Shortens the In-Context Learning (ICL) Plateau Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​The growing demand for personalized and private on-device applications highlights the importance of source-free unsupervised domain adaptation (SFDA) methods, especially for time-series data, where individual differences produce large domain shifts. As sensor-embedded mobile devices become ubiquitous, optimizing SFDA methods for parameter utilization and data-sample efficiency… Read More »Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement Apple Machine Learning Research

​[[{“value”:” The discovery of new materials is crucial to addressing pressing global challenges such as climate change and advancements in next-generation computing. However, existing computational and experimental approaches face significant limitations in efficiently exploring the vast chemical space. While AI has emerged as a powerful… Read More »Meta AI Releases Meta’s Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The growing reliance on large language models for coding support poses a significant problem: how best to assess real-world impact on programmer productivity? Current approaches, such as static bench-marking based on datasets such as HumanEval, measure the correctness of the code but cannot capture… Read More »RealHumanEval: A Web Interface to Measure the Ability of LLMs to Assist Programmers Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In the rapidly evolving world of AI, challenges related to scalability, performance, and accessibility remain central to the efforts of research communities and open-source advocates. Issues such as the computational demands of large-scale models, the lack of diverse model sizes for different use cases,… Read More »Open Collective Releases Magnum/v4 Series Models From 9B to 123B Parameters Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” 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… Read More »CREAM: A New Self-Rewarding Method that Allows the Model to Learn more Selectively and Emphasize on Reliable Preference Data Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Human beings possess innate extraordinary perceptual judgments, and when computer vision models are aligned with them, model’s performance can be improved manifold. Various attributes such as scene layout, subject location, camera pose, color, perspective, and semantics help us have a clear picture of the… Read More »This AI Paper Explores If Human Visual Perception can Help Computer Vision Models Outperform in Generalized Tasks Adeeba Alam Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” 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… Read More »Differentiable Rendering of Robots (Dr. Robot): A Robot Self-Model Differentiable from Its Visual Appearance to Its Control Parameters Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​[[{“value”:” 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… Read More »Self-Data Distilled Fine-Tuning: A Solution for Pruning and Supervised Fine-tuning Challenges in LLMs Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” 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… Read More »Rethinking Direct Alignment: Balancing Likelihood and Diversity for Better Model Performance Asif Razzaq Artificial Intelligence Category – MarkTechPost