Natural Language Processing
Machine Learning
Generative Pretrained Transformers
Large Language Models
Artificial Intelligence
Prompt Engineering
Information Retrieval
Published on July 23, 2024 by
Daniel Hofheinz
In today’s fast-paced world, the way we work is constantly evolving. With the emergence of generative AI, enterprises are increasingly turning to chatbots to enhance productivity and streamline communication. But not all chatbots are created equal, and building one that meets the unique needs of a business can be quite the challenge. A recent research paper titled "FACTS About Building Retrieval Augmented Generation-based Chatbots" dives deep into this topic, offering a comprehensive guide for organizations looking to harness the power of chatbots.
So, what makes a chatbot truly effective? The authors highlight that it all starts with a framework known as Retrieval Augmented Generation, or RAG for short. This innovative approach combines the capabilities of Large Language Models (LLMs), such as those developed by NVIDIA, with orchestration frameworks like Langchain and Llamaindex. Together, these tools form the b...
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Natural Language Processing
Machine Learning
Generative Pretrained Transformers
Large Language Models
Artificial Intelligence
Published on July 23, 2024 by
Daniel Hofheinz
In a world increasingly reliant on technology and artificial intelligence, we often find ourselves pondering the implications of these advancements, especially when it comes to critical fields like healthcare. A recent study published on arXiv sheds light on a pressing issue: the presence of bias in large language models (LLMs) when evaluating medical professionals. This study serves as a wake-up call, urging us to consider how these powerful tools might influence the future of medical recruitment and, by extension, the healthcare workforce.
The researchers behind this study took a meticulous approach to evaluate whether biases exist within LLMs like GPT-4, Claude-3-haiku, and Mistral-Large when assessing fictitious candidate resumes for residency programs. By controlling for identity factors while keeping qualifications consistent, the researchers created an intricate testing environment. They tested for both ex...
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Natural Language Processing
Machine Learning
Generative Pretrained Transformers
Large Language Models
Artificial Intelligence
Published on July 23, 2024 by
Daniel Hofheinz
In recent years, Large Language Models (LLMs) have taken the world by storm, revolutionizing our approach to natural language processing (NLP). From chatbots to content creation, these models have proven their ability to understand and generate human-like text with remarkable proficiency. But as our demands for increasingly complex reasoning grow, there is one critical aspect that remains underexplored: knowledge reasoning. How can we derive new knowledge from existing data, especially when faced with challenges like rule overfitting? A recent research paper introduces an innovative framework called Chain-of-Knowledge (CoK), aiming to tackle these very questions.
The authors of the paper, titled Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs, delve into the world of knowledge reasoning, a process that seeks to uncover new insights from established...
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Natural Language Processing
Machine Learning
Large Language Models
Published on July 18, 2024 by
Daniel Hofheinz
In our rapidly evolving digital landscape, where data is king, the efficiency and privacy of machine learning models have become paramount. One fascinating area of research that is making waves is federated learning, a method that allows models to learn from data distributed across various devices without the need to share sensitive information. But here's the catch: to truly harness the power of federated learning, we need to address data deduplication—a critical preprocessing step that has historically posed significant challenges.
A recent paper titled "Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models" dives deep into this subject, presenting a groundbreaking approach known as Efficient Privacy-Preserving Multi-Party Deduplication (EP-MPD). This innovative protocol not only enhances the performance of machine learning models but does so while safeguarding user privacy,...
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Natural Language Processing
Machine Learning
Generative Pretrained Transformers
Large Language Models
Artificial Intelligence
Published on July 18, 2024 by
Daniel Hofheinz
In the rapidly evolving landscape of artificial intelligence, Transformer-based Large Language Models (LLMs) have emerged as game-changers. Their ability to perform exceptionally across various tasks—from natural language understanding to text generation—has sparked intense interest in both academic and industrial circles. However, as these models grow in complexity, training them efficiently on long sequences becomes a daunting challenge. This is where the innovative concept of WallFacer comes into play, promising to revolutionize how we approach this problem.
Imagine trying to solve a complex puzzle where every piece influences the others. This is akin to the n-body problem in physics, which deals with predicting the individual motions of a group of celestial objects interacting with each other. In the context of Transformers, the attention mechanism can be viewed similarly: each token in a sequence interacts w...
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