AWS AI Labs has unveiled CODE SAGE, a groundbreaking bidirectional encoder representation model for programming code. It uses a two-stage training scheme and a vast dataset to enhance comprehension and manipulation of code. This model outperforms existing ones in code-related tasks and opens new possibilities for deep learning in understanding and utilizing programming languages.

Meta researchers have developed V-JEPA, a non-generative AI model aimed at enhancing the reasoning and planning abilities of machine intelligence. Utilizing self-supervised learning and a frozen evaluation approach, V-JEPA efficiently learns from unlabeled data and excels in various video analysis tasks. It outperforms previous methods in fine-grained action recognition and other tasks.

Google DeepMind’s research has led to a significant advancement in length generalization for transformers. Their approach, featuring the FIRE position encoding and a reversed data format, enables transformers to effectively process much longer sequences with notable accuracy. This breakthrough holds promise for expanding the practical applications and capabilities of language models in artificial intelligence.

Large language models (LLMs) aligning with human expectations is crucial for societal benefits. Reinforcement learning from human feedback (RLHF) and direct alignment from preferences (DAP) are approaches discussed. A new study introduces Online AI Feedback (OAIF) for DAP, combining online flexibility and efficiency. Empirical comparisons demonstrate OAIF’s effectiveness, especially in aligning LLMs online.

This research from UC Berkeley analyzes the evolving role of large language models (LLMs) in the digital ecosystem, highlighting the complexities of in-context reward hacking (ICRH). It discusses the limitations of static benchmarks in understanding LLM behavior and proposes dynamic evaluation recommendations to anticipate and mitigate risks. The study aims to enhance the development of…

Infographics and user interfaces share design concepts and visual languages. To address the complexity of each, Google Research introduced ScreenAI, a Vision-Language Model (VLM) capable of comprehending UIs and infographics. ScreenAI achieved remarkable performance on various tasks and released three new datasets to advance the field. Learn more in the research paper.

Large Language Models (LLMs) such as GPT, PaLM, and LLaMa have enhanced AI and NLP by enabling machines to comprehend and produce human-like content. Finetuning is crucial to adapt these generalist models to specialized activities. Approaches include Parameter Efficient Fine Tuning (PEFT), Supervised Finetuning with hyperparameter tweaking, transfer learning, and few-shot learning, and Reinforcement Learning…

This survey explores the burgeoning field of prompt engineering, which leverages task-specific instructions to enhance the adaptability and performance of language and vision models. Researchers present a systematic overview of over 29 techniques, categorizing advancements by application area and emphasizing the transformative impact of prompt engineering on model capabilities. Despite notable successes, challenges such as…

Studying scaling laws in large language models is crucial for optimizing their performance in tasks like translation. Challenges include determining the impact of pretraining data size on downstream tasks and developing strategies to enhance model performance. New scaling laws by researchers predict translation quality based on pretraining data size, offering insights for effective model training…

Reinforcement learning encompasses model-based (MB) and model-free (MF) algorithms. The Diffusion World Model (DWM) is a novel approach addressing inaccuracies in world modeling. DWM predicts long-horizon outcomes and enhances RL performance. By combining MB and MF strengths, DWM achieves state-of-the-art results, bridging the gap between the two approaches. This new framework presents promising advancements in…