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Large language models (LLMs) have profoundly transformed the landscape of artificial intelligence (AI) in natural language processing (NLP). These models can understand and generate human-like text, representing a pinnacle of current AI research. Yet, the computational intensity required for their operation, particularly during inference, presents a formidable challenge. This issue is exacerbated as models grow…

The emergence of Large Language Models (LLMs) has transformed the landscape of natural language processing (NLP). The introduction of the transformer architecture marked a pivotal moment, ushering in a new era in NLP. While a universal definition for LLMs is lacking, they are generally understood as versatile machine learning models adept at simultaneously handling various…

Self-supervised learning (SSL) has proven to be an indispensable technique in AI, particularly in pretraining representations on vast, unlabeled datasets. This significantly reduces the dependency on labeled data, often a major bottleneck in machine learning. Despite the merits, a major challenge in SSL, particularly in Joint Embedding (JE) architectures, is evaluating the quality of learned…

Generative AI has recently seen a boom, with large language models (LLMs) showing broad applicability across many fields. These models have improved the performance of numerous tools, including those that facilitate interactions based on searches, program synthesis, chat, and many more. Also, language-based methods have made it easier to link many modalities, which has led…

Processing extensive sequences of linguistic data has been a significant hurdle, with traditional transformer models often buckling under the weight of computational and memory demands. This limitation is primarily due to the quadratic complexity of the attention mechanisms these models rely on, which scales poorly as sequence length increases. The introduction of State Space Models…

In an era where language models (LMs) predominantly cater to English, a revolutionary stride has been made with the introduction of CroissantLLM. This model bridges the linguistic divide by offering robust bilingual capabilities in both English and French. This development marks a significant departure from conventional models, often biased towards English, limiting their applicability in…

Recent advancements in large language models (LLMs) have propelled the field forward in interpreting and executing instructions. Despite these strides, LLMs still grapple with errors in recalling and composing world knowledge, leading to inaccuracies in responses. To address this, the integration of auxiliary tools, such as using search engines or calculators during inference, has been…

In an era where digital privacy has become paramount, the ability of artificial intelligence (AI) systems to forget specific data upon request is not just a technical challenge but a societal imperative. The researchers have embarked on an innovative journey to tackle this issue, particularly within image-to-image (I2I) generative models. These models, known for their…

[et_pb_section admin_label=”section”] [et_pb_row admin_label=”row”] [et_pb_column type=”4_4″][et_pb_text admin_label=”Text”] In the rapidly evolving data analysis landscape, the quest for robust time series forecasting models has taken a novel turn with the introduction of TIME-LLM, a pioneering framework developed by a collaboration between esteemed institutions, including Monash University and Ant Group. This framework departs from traditional approaches by…

Significant improvements have been made in enhancing the accuracy and efficiency of Automatic Speech Recognition (ASR) systems. The recent research delves into integrating an external Acoustic Model (AM) into End-to-End (E2E) ASR systems, presenting an approach that addresses the persistent challenge of domain mismatch – a common obstacle in speech recognition technology. This methodology by…