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Language models are fundamental to natural language processing (NLP), focusing on generating and comprehending human language. These models are integral to applications such as machine translation, text summarization, and conversational agents, where the aim is to develop technology capable of understanding and producing human-like text. Despite their significance, the effective evaluation of these models remains…
Large Language Models (LLMs) have driven remarkable advancements across various Natural Language Processing (NLP) tasks. These models excel in understanding and generating human-like text, playing a pivotal role in applications such as machine translation, summarization, and more complex reasoning tasks. The progression in this field continues to transform how machines comprehend and process language, opening…
Parameter-efficient fine-tuning (PEFT) techniques adapt large language models (LLMs) to specific tasks by modifying a small subset of parameters, unlike Full Fine-Tuning (FFT), which updates all parameters. PEFT, exemplified by Low-Rank Adaptation (LoRA), significantly reduces memory requirements by updating less than 1% of parameters while achieving similar performance to FFT. LoRA uses low-rank matrices to…
Unlocking the potential of large multimodal language models (MLLMs) to handle diverse modalities like speech, text, image, and video is a crucial step in AI development. This capability is essential for applications such as natural language understanding, content recommendation, and multimodal information retrieval, enhancing the accuracy and robustness of AI systems. Traditional methods for handling…
GPT-4 and other Large Language Models (LLMs) have proven to be highly proficient in text analysis, interpretation, and generation. Their exceptional effectiveness extends to a wide range of financial sector tasks, including sophisticated disclosure summarization, sentiment analysis, information extraction, report production, and compliance verification. However, studies have been still going on about their function in…
Advancements in AI have led to proficient systems that make unclear decisions, raising concerns about deploying untrustworthy AI in daily life and the economy. Understanding neural networks is vital for trust, ethical concerns like algorithmic bias, and scientific applications requiring model validation. Multilayer perceptrons (MLPs) are widely used but lack interpretability compared to attention layers.…
Foundation models are central to AI’s influence on the economy and society. Transparency is crucial for accountability, competition, and understanding, particularly regarding the data used in these models. Governments are enacting regulations like the EU AI Act and the US AI Foundation Model Transparency Act to enhance transparency. The Foundation Model Transparency Index (FMTI) introduced…
People who work with large language models often need a quick and efficient way to interact with these powerful tools. However, many existing methods require switching between applications or dealing with slow, cumbersome interfaces. Some solutions are available, but they come with their own set of limitations. Web-based interfaces are common but can be slow…
The memory footprint of the key-value (KV) cache can be a bottleneck when serving large language models (LLMs), as it scales proportionally with both sequence length and batch size. This overhead limits batch sizes for long sequences and necessitates costly techniques like offloading when on-device memory is scarce. Furthermore, the ability to persistently store and…
Cryptocurrency systems have evolved significantly since Bitcoin was introduced in 2009. As the landscape becomes increasingly complex, integrating artificial intelligence (AI) offers promising avenues for enhancing security and efficiency. AI’s capabilities in data analysis, pattern recognition, and predictive modeling make it a critical tool for addressing the unique challenges advanced cryptocurrency systems face. Image Source…