The potential of machine learning research to create a lasting impact in academic research is clear. Recent developments in the field have seen a surge of new techniques and approaches that are pushing the boundaries of what is possible. From quantum tensor networks for sequence processing to Memory-and-Anticipation Transformers for online action detection and anticipation tasks, the potential for these techniques to revolutionize the field is immense. This newsletter presents a survey of the latest developments in machine learning research, with a focus on potential breakthroughs. We will explore evaluation techniques for large language models, quantum tensor networks for sequence processing, robustness of successive versions of Large Language Models (LLMs) against adversarial attacks, a novel approach to named entity recognition, a retrieval-augmented encoder-decoder language model, a new learning framework for neural networks, a two-stage architecture for relevance modeling in Meituan search, Graph Neural Networks (GNNs) for edge regression tasks in agriculture trade between nations, and a GraphLayoutLM model for document understanding. By exploring these recent developments, we can gain a better understanding of the potential
This paper presents a survey of evaluation techniques for large language models, with the potential to create a lasting impact in academic research. It proposes a competency architecture to better evaluate LLMs, combining similar tasks and allowing for new tasks to be added. It also provides suggestions for future directions of LLM evaluation.
This paper introduces quantum tensor networks for sequence processing, which can be used to efficiently classify and generate sequences with long-range correlations. The potential for these techniques to create a lasting impact in academic research is demonstrated through experiments on real-world datasets and implementation on a quantum processor.
This study examines the robustness of successive versions of Large Language Models (LLMs) against adversarial attacks. It finds that updated versions of LLMs do not exhibit the expected level of robustness, and that synergized adversarial queries are more effective. The findings have potential to create a lasting impact in academic research, by providing valuable insights into LLMs for developers and users.
This paper presents iNERD, a novel approach to named entity recognition that leverages the language understanding capabilities of generative models. It employs an informed decoding scheme to improve performance and eliminate hallucinations, and achieves remarkable results on eight datasets. This technique has the potential to create a lasting impact in academic research, by providing a future-proof solution to information extraction tasks.
This paper presents RAVEN, a retrieval-augmented encoder-decoder language model that enables in-context learning with improved performance and fewer parameters. The proposed Fusion-in-Context Learning technique has the potential to create a lasting impact in academic research by providing a more efficient and effective way to leverage in-context examples.
MOLE is a new learning framework for neural networks that modularizes them and optimizes each module via mutual information maximization. This framework has been experimentally proven to be universally applicable to different types of data, and has the potential to create a lasting impact in academic research by providing a more biologically plausible training scheme than backpropagation.
This paper presents a two-stage architecture for relevance modeling in Meituan search, which leverages rich structured documents and domain knowledge to improve user experience. The proposed techniques have been successfully deployed online, and have had a lasting impact on academic research.
This paper presents a case study on the application of Graph Neural Networks (GNNs) to edge regression tasks in agriculture trade between nations. Results show that existing GNNs are inadequate for this task, but the proposed TGN model outperforms other GNN models. This research has the potential to create a lasting impact in academic research of temporal edge regression techniques.
This paper presents GraphLayoutLM, a novel document understanding model that leverages the modeling of layout structure graph to inject document layout knowledge into the model. The proposed model enables the understanding of the spatial arrangement of text elements, improving document comprehension and achieving state-of-the-art results on various benchmarks. The potential for this technique to create a lasting impact in academic research is clear, as it provides a significant improvement over existing approaches.
This paper presents Memory-and-Anticipation Transformer (MAT), a novel memory-anticipation-based approach for online action detection and anticipation tasks. MAT surpasses existing methods in four challenging benchmarks, and has the potential to create a lasting impact in academic research.