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 human preference alignment under RLHF ensures that their behaviors are ethical…
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 where human collaboration and explainability are essential. For instance, in a…
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 benchmarks is time-consuming, and interpreting results across multiple evaluation metrics becomes…
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 Learning from Human Feedback (RLHF). However, LLMs need help generating appropriate…
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 this field has prompted researchers to explore the integration of artificial…
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 answers appear verbatim in training data. Achieving low hallucination rates demands…
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 computational resources. For example, training the LLaMA 65B model took 21…
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 tasks or preferences. Therefore, This process has led to great advances…
AI-related risks concern policymakers, researchers, and the general public. Although substantial research has identified and categorized these risks, a unified framework is needed to be consistent with terminology and clarity. This lack of standardization makes it challenging for organizations to create thorough risk mitigation strategies and for policymakers to enforce effective regulations. The variation in…
The number of scientific publications is rapidly growing, increasing each year by 4%-5%. This poses a major challenge for researchers who spend most of their time reviewing numerous academic papers to keep updated with their fields. This is essential for staying relevant and innovative in research but can be inefficient and time-consuming. To tackle these…