​[[{“value”:” Language model alignment is quite important, particularly in a subset of methods from RLHF that have been applied to strengthen the safety and competence of AI systems. Language models are deployed in many applications today, and their outputs can be harmful or biased. Inherent… Read More »USC Researchers Present Safer-Instruct: A Novel Pipeline for Automatically Constructing Large-Scale Preference Data Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Future reward estimation is crucial in RL as it predicts the cumulative rewards an agent might receive, typically through Q-value or state-value functions. However, these scalar outputs lack detail about when or what specific rewards the agent anticipates. This limitation is significant in applications… Read More »Enhancing Reinforcement Learning Explainability with Temporal Reward Decomposition Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Vision-language models (VLMs) have gained significant attention due to their ability to handle various multimodal tasks. However, the rapid proliferation of benchmarks for evaluating these models has created a complex and fragmented landscape. This situation poses several challenges for researchers. Implementing protocols for numerous… Read More »UniBench: A Python Library to Evaluate Vision-Language Models VLMs Robustness Across Diverse Benchmarks Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have gained prominence in deep learning, demonstrating exceptional capabilities across various domains such as assistance, code generation, healthcare, and theorem proving. The training process for LLMs typically involves two stages: pretraining with massive corpora and an alignment step using Reinforcement… Read More »Meta AI and NYU Researchers Propose E-RLHF to Combat LLM Jailbreaking Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Extended Reality (XR) technology transforms how users interact with digital environments, blending the physical and virtual worlds to create immersive experiences. XR devices are equipped with advanced sensors that capture rich streams of user data, enabling personalized and context-aware interactions. The rapid evolution of… Read More »EmBARDiment: An Implicit Attention Framework that Enhances AI Interaction Efficiency in Extended Reality Through Eye-Tracking and Contextual Memory Integration Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Language models (LMs) exhibit improved performance with increased size and training data, yet the relationship between model scale and hallucinations remains unexplored. Defining hallucinations in LMs presents challenges due to their varied manifestations. A new study from Google Deepmind focuses on hallucinations where correct… Read More »Understanding Hallucination Rates in Language Models: Insights from Training on Knowledge Graphs and Their Detectability Challenges Shoaib Nazir Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have gained significant attention due to their remarkable performance across various tasks, revolutionizing research paradigms. However, the training process for these models faces several challenges. LLMs depend on static datasets and undergo long training periods, which require a lot of… Read More »Aquila2: Advanced Bilingual Language Models Ranging from 7 to 70 Billion Parameters Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have considerably altered the landscape of natural language processing, enabling machines to understand and generate human language much more effectively than ever. Normally, these models are pre-trained on huge and parallel corpora and then fine-tuned to connect them to human… Read More »This AI Paper from John Hopkins Introduces Continual Pre-training and Fine-Tuning for Enhanced LLM Performance Nikhil Artificial Intelligence Category – MarkTechPost