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Large Language Models Natural Language Processing Webinar 
Marktechpost
JANUARY 11, 2025
Large Language Models (LLMs) have shown remarkable capabilities across diverse natural language processing tasks, from generating text to contextual reasoning. The post SepLLM: A Practical AI Approach to Efficient Sparse Attention in Large Language Models appeared first on MarkTechPost.
AI News
FEBRUARY 13, 2025
Recent benchmarks from Hugging Face, a leading collaborative machine-learning platform, position Qwen at the forefront of open-source large language models (LLMs). The technical edge of Qwen AI Qwen AI is attractive to Apple in China because of the former’s proven capabilities in the open-source AI ecosystem.
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How to Achieve High-Accuracy Results When Using LLMs
Relevance, Reach, Revenue: How to Turn Marketing Trends From Hype to High-Impact
AI News
FEBRUARY 15, 2024
Researchers at Amazon have trained a new large language model (LLM) for text-to-speech that they claim exhibits “emergent” abilities. The 980 million parameter model, called BASE TTS, is the largest text-to-speech model yet created.
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Marktechpost
JANUARY 12, 2025
Large language models ( LLMs ) like GPT-4, PaLM, Bard, and Copilot have made a huge impact in natural language processing (NLP). These models require vast computational resources, making them expensive to train and deploy. The post What are Small Language Models (SLMs)?
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Marktechpost
JULY 22, 2024
Large Language Models (LLMs) have revolutionized natural language processing, demonstrating remarkable capabilities in various applications. Transformer architecture has emerged as a major leap in natural language processing, significantly outperforming earlier recurrent neural networks.
Marktechpost
SEPTEMBER 17, 2024
Prior research on Large Language Models (LLMs) demonstrated significant advancements in fluency and accuracy across various tasks, influencing sectors like healthcare and education. This progress sparked investigations into LLMs’ language understanding capabilities and associated risks.
Marktechpost
JULY 26, 2024
Large language models’ (LLMs) training pipelines are the source of inspiration for this method in the field of natural language processing (NLP). Tokenizing input is a crucial part of LLM training, and it’s commonly accomplished using byte pair encoding (BPE).
Marktechpost
AUGUST 31, 2024
Large Language Models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks. However, they face a significant challenge: hallucinations, where the models generate responses that are not grounded in the source material.
Marktechpost
SEPTEMBER 20, 2024
Multimodal large language models (MLLMs) focus on creating artificial intelligence (AI) systems that can interpret textual and visual data seamlessly. The NVLM-H model, in particular, strikes a balance between image processing efficiency and multimodal reasoning accuracy, making it one of the most promising models in this field.
Marktechpost
SEPTEMBER 22, 2024
In artificial intelligence and natural language processing, long-context reasoning has emerged as a crucial area of research. As the volume of information that needs to be processed grows, machines must be able to synthesize and extract relevant data from massive datasets efficiently.
Marktechpost
SEPTEMBER 23, 2024
Large language models (LLMs) have gained significant attention due to their potential to enhance various artificial intelligence applications, particularly in natural language processing. In conclusion, this research addresses a critical issue in deploying large language models in real-world applications.
AI News
OCTOBER 18, 2024
Decentralised AI systems built on blockchains can help to democratise access to essential AI resources like computing power, data, and large language models. They are sorely needed too; as AI models become more powerful, their thirst for data and computing power grows, increasing the barrier of entry to the industry.
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Marktechpost
DECEMBER 8, 2024
The field of natural language processing (NLP) has grown rapidly in recent years, creating a pressing need for better datasets to train large language models (LLMs). Existing resources like CC-100, mC4, CulturaX, and HPLT provide useful starting points but come with notable drawbacks.
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NOVEMBER 20, 2024
The effectiveness of RAG heavily depends on the quality of context provided to the large language model (LLM), which is typically retrieved from vector stores based on user queries. The relevance of this context directly impacts the model’s ability to generate accurate and contextually appropriate responses.
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Marktechpost
OCTOBER 18, 2024
Recent advancements in Large Language Models (LLMs) have reshaped the Artificial intelligence (AI)landscape, paving the way for the creation of Multimodal Large Language Models (MLLMs). If you like our work, you will love our newsletter. Don’t Forget to join our 50k+ ML SubReddit.
Marktechpost
AUGUST 28, 2024
Sarcasm detection is a critical challenge in natural language processing (NLP) because of sarcastic statements’ nuanced and often contradictory nature. Unlike straightforward language, sarcasm involves saying something that appears to convey one sentiment while implying the opposite.
Marktechpost
NOVEMBER 16, 2024
Large language models (LLMs) have revolutionized natural language processing by offering sophisticated abilities for a range of applications. However, these models face significant challenges. Modulizing LLMs into functional bricks optimizes computational efficiency, scalability, and flexibility.
Marktechpost
SEPTEMBER 11, 2024
One important tactic for improving large language models’ (LLMs’) capacity for reasoning is the Chain-of-Thought (CoT) paradigm. By encouraging models to divide tasks into intermediate steps, much like humans methodically approach complex problems, CoT improves the problem-solving process.
Marktechpost
SEPTEMBER 18, 2024
Artificial intelligence (AI) and natural language processing (NLP) have seen significant advancements in recent years, particularly in the development and deployment of large language models (LLMs). If you like our work, you will love our newsletter.
Marktechpost
JULY 22, 2024
The recent development of large language models (LLMs) has transformed the field of Natural Language Processing (NLP). LLMs show human-level performance in many professional and academic fields, showing a great understanding of language rules and patterns.
Marktechpost
JULY 28, 2024
In the rapidly evolving field of natural language processing (NLP), integrating external knowledge bases through Retrieval-Augmented Generation (RAG) systems represents a significant leap forward. Don’t Forget to join our 47k+ ML SubReddit Find Upcoming AI Webinars here The post Is the Future of Agentic AI Personal?
Marktechpost
AUGUST 14, 2024
70b by Mobius Labs, boasting 70 billion parameters, has been designed to enhance the capabilities in natural language processing (NLP), image recognition, and data analysis. Mobius Labs, known for its cutting-edge innovations, has positioned this model as a cornerstone in the next generation of AI technologies.
Marktechpost
JULY 30, 2024
Prior work on abstention in large language models (LLMs) has made significant strides in query processing, answerability assessment, and handling misaligned queries. They present a framework analyzing abstention from the query, model, and human value perspectives.
Marktechpost
SEPTEMBER 2, 2024
Recent developments in machine learning, particularly in natural language processing (NLP), have significantly enhanced the capabilities of IR systems. This significant imbalance highlights the difficulty in capturing the full complexity of query-document relationships, particularly in large datasets.
Marktechpost
AUGUST 9, 2024
Recent studies have highlighted significant achievements in powerful closed-source large language models (LLMs) like GPT-4, which use advanced prompting techniques. This method enhances domain generalization in text-to-SQL models and explores the potential of using weak data supervision through preference learning.
Marktechpost
OCTOBER 23, 2024
Considering the major influence of autoregressive ( AR ) generative models, such as Large Language Models in natural language processing ( NLP ), it’s interesting to explore whether similar approaches can work for images. If you like our work, you will love our newsletter.
Marktechpost
OCTOBER 19, 2024
The rise of Transformer-based models has significantly advanced the field of natural language processing. However, the training of these models is often computationally intensive, requiring substantial resources and time. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.
Marktechpost
JULY 24, 2024
This research paper addresses the limitations of existing agentic frameworks in natural language processing (NLP) tasks, particularly the inefficiencies in handling dynamic and complex queries that require context refinement and interactive problem-solving. If you like our work, you will love our newsletter.
Marktechpost
SEPTEMBER 22, 2024
Chain-of-thought (CoT) prompting has emerged as a popular technique to enhance large language models’ (LLMs) problem-solving abilities by generating intermediate steps. Despite its better performance in mathematical reasoning, CoT’s effectiveness in other domains remains questionable. Check out the Paper.
Marktechpost
JULY 26, 2024
These models, enhanced by pre-trained language models (PLMs), set the state-of-the-art in the field, benefiting from large-scale corpora to improve their linguistic capabilities. These LLMs, with their substantial number of parameters, can capture complex patterns in data, making them well-suited for the Text-to-SQL task.
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AWS Machine Learning Blog
AUGUST 16, 2023
Thomson Reuters Labs, the company’s dedicated innovation team, has been integral to its pioneering work in AI and natural language processing (NLP). A key milestone was the launch of Westlaw Is Natural (WIN) in 1992. This technology was one of the first of its kind, using NLP for more efficient and natural legal research.
Marktechpost
AUGUST 22, 2024
The field of natural language processing has made substantial strides with the advent of Large Language Models (LLMs), which have shown remarkable proficiency in tasks such as question answering. However, despite their success, LLMs need help dealing with knowledge-intensive queries.
Marktechpost
AUGUST 12, 2024
Large Language Models (LLMs), like ChatGPT and GPT-4 from OpenAI, are advancing significantly and transforming the field of Natural Language Processing (NLP) and Natural Language Generation (NLG), thus paving the way for the creation of a plethora of Artificial Intelligence (AI) applications indispensable to daily life.
Marktechpost
SEPTEMBER 2, 2024
In natural language processing (NLP), handling long text sequences effectively is a critical challenge. Traditional transformer models, widely used in large language models (LLMs), excel in many tasks but must be improved when processing lengthy inputs. Check out the Paper.
Marktechpost
JULY 27, 2024
Large language models (LLMs) are used in various applications, such as machine translation, summarization, and content creation. These hallucinations can manifest as incorrect facts or misrepresentations, impacting the model’s utility in sensitive applications. If you like our work, you will love our newsletter.
Marktechpost
OCTOBER 19, 2024
Large language models (LLMs) like GPT-4, Gemini, and Llama 3 have revolutionized natural language processing through extensive pre-training and supervised fine-tuning (SFT). However, these models come with high computational costs for training and inference.
Marktechpost
AUGUST 16, 2024
Large Language Models (LLMs) have achieved remarkable progress in the ever-expanding realm of artificial intelligence, revolutionizing natural language processing and interaction. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.
Marktechpost
AUGUST 13, 2024
Recent advances in Artificial Intelligence and Machine Learning have made tremendous progress in handling many computing jobs possible, especially with the introduction of Large Language Models (LLMs). Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.
Marktechpost
OCTOBER 21, 2024
AI models are built upon large language models (LLMs), designed specifically for enterprise AI applications. These include 8B and 2B parameter-dense decoder-only models, which outperformed similarly sized Llama-3.1 2B and 8B AI Models for AI Enterprises appeared first on MarkTechPost.
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Marktechpost
SEPTEMBER 20, 2024
Large language models (LLMs) have made significant leaps in natural language processing, demonstrating remarkable generalization capabilities across diverse tasks. This study introduces Sketch , a significant advancement in simplifying and optimizing the applications of large language models.
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Marktechpost
SEPTEMBER 16, 2024
These graphs map entities and their relationships in a structured form, which is useful for applications like reasoning, information retrieval, and natural language processing. On the other hand, neural systems, particularly Large Language Models (LLMs), offer expansive knowledge through deep learning.
Marktechpost
JULY 27, 2024
Generative Artificial Intelligence (GenAI), particularly large language models (LLMs) like ChatGPT, has revolutionized the field of natural language processing (NLP). Their ability to generate human-like text stems from training on vast datasets and leveraging deep learning architectures.
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Marktechpost
JULY 25, 2024
Question answering (QA) is a crucial area in natural language processing (NLP), focusing on developing systems that can accurately retrieve and generate responses to user queries from extensive data sources. This limitation hampers evaluating how well LLMs can generalize across different domains.
Marktechpost
AUGUST 25, 2024
These findings highlight the impact of model size on RAG performance and provide valuable insights for natural language processing research. By providing a standardized approach for assessment and a platform for innovation, RAGLAB is poised to become an essential tool for natural language processing researchers.
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