The field of machine learning research is constantly evolving, with new breakthroughs and discoveries being made every day. From LongQLoRA to DACBERT, the potential for lasting impact in academic research of the described techniques is high. In this newsletter, we will explore some of the most recent developments in machine learning research and discuss the potential for these breakthroughs to create a lasting impact. LongQLoRA is a new method to extend context length of large language models with less training resources, achieving competitive perplexity performance and outperforming LongLoRA. GBLM-Pruner is a novel sparsity-centric pruning method for pretrained LLMs that leverages gradients to determine the importance pruning score. It has been shown to outperform competitive counterparts and has the potential to create a lasting impact in academic research of the described techniques. This paper investigates the ability of large language models to generalize linguistic knowledge, focusing on argument structure. Results show that while the models can generalize between related contexts seen during pre-training, they fail to generalize between more abstract, but well-attested structural
LongQLoRA is a new method to extend context length of large language models with less training resources, achieving competitive perplexity performance and outperforming LongLoRA. With LongQLoRA, the potential for lasting impact in academic research of the described techniques is high.
This paper presents GBLM-Pruner, a novel sparsity-centric pruning method for pretrained LLMs that leverages gradients to determine the importance pruning score. It has been shown to outperform competitive counterparts and has the potential to create a lasting impact in academic research of the described techniques.
This paper investigates the ability of large language models to generalize linguistic knowledge, focusing on argument structure. Results show that while the models can generalize between related contexts seen during pre-training, they fail to generalize between more abstract, but well-attested structural generalizations. This suggests a limitation of current models and highlights the need for data-intensive training.
This paper presents a new gated linear RNN model, HGRN, which uses forget gates to model both short-term and long-term dependencies. Experiments show that HGRN is efficient and effective, with potential to create a lasting impact in academic research.
This paper presents a novel approach to studying human memory for meaningful narratives using large language models. Results from online experiments show that both recognition and recall performance scale linearly with narrative length, and that even when stories are scrambled, recognition remains largely unaffected. This suggests that language models can be used to create lasting impact in academic research of memory and comprehension.
This paper proposes a generative neuro-symbolic visual reasoning model that can grow and reuse modules to achieve strong visual reasoning results while maintaining efficiency. It has the potential to create a lasting impact in academic research by providing competitive performance on standard tasks, seamless transferability of modules, and the ability to adapt to new tasks with few training examples.
This paper presents a versatile architecture for geometric deep learning, GATr, which can be adapted to any geometric algebra. It evaluates the potential of Euclidean, projective, and conformal algebras for 3D data, and finds that the conformal algebra and an improved version of the projective algebra are the most powerful and performant. This could have a lasting impact on academic research, as it provides a scalable and efficient way to process 3D data.
This paper presents a novel technique, "Future Lens", which uses linear approximation and causal intervention methods to accurately predict several tokens ahead from a single hidden state. The results show that a single hidden state can contain signal rich enough to predict future tokens with more than 48% accuracy. This technique has the potential to create a lasting impact in academic research by providing a new view of transformer states.
This paper explores the potential of using data from scientific papers to train CLIP models, which could lead to improved performance and a range of applications. Experiments on small-scale models show that model performance increases, suggesting that training large-scale CLIP models with this data could have a lasting impact in academic research.
DACBERT introduces a novel two-stage pretraining framework that combines syntax and semantic information to improve performance and interpretability of BERT models. Evaluations on GLUE benchmark show significant improvement, with an average GLUE score increase of 0.83%. The pretraining process is cost-efficient and can be completed within 24 hours on a single GPU, making it a promising tool for lasting impact in academic research.