research-papers
Original Paper: https://arxiv.org/abs/2310.06839 By: Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, Lili Qiu Abstract: In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the
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Retrieval-augmented generation (RAG) is a significant advancement in the ability of large language models (LLMs) to utilize external knowledge for more accurate and contextually rich responses. However, in standard RAG systems, small text chunks are used to isolate precise information for retrieval. This approach works well for focused facts but
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In our previous articles, we've discussed the importance of RAG and how it connects LLMs to external data sources. This approach improves the accuracy and relevance of LLM outputs, especially when dealing with domain-specific topics. Traditional RAG systems retrieve k-documents for a query and use them to construct
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Introduction In the quickly changing technology landscape of today, artificial intelligence (AI) is a game-changer for companies in a wide range of industries. But what is the hidden energy source for these clever systems? The foundation of contemporary AI applications, AI infrastructure, holds the key to the solution. What is
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AI agents like ChatGPT are advanced systems that operate independently, making decisions and taking actions without needing human intervention. They are becoming increasingly important in today's technology-driven world, as they have the potential to revolutionize various industries. These agents are capable of learning, adapting, and improving over time,
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Chain of Thought Prompting (CoT) - Overview An artificial intelligence method called Chain of Thought prompting encourages sequential thinking, which enables models to handle challenging tasks more effectively. CoT prompting, which mimics human-like problem-solving, allows AI systems to break down difficulties into smaller, more manageable portions. This organized method enhances
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Introduction Technology is constantly changing the way we interact with it, and one of the most exciting areas of progress is artificial intelligence (AI). Human-like thought, learning, and communication are increasingly being taught to machines. Among these developments, prompt engineering is a particular ability that is becoming more and more
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Introduction AI agent frameworks are advanced tools that developers use to build intelligent systems capable of autonomous decision-making and task execution. These frameworks serve as a foundation, allowing developers to focus on high-level system behavior rather than manually coding every aspect of the agents. By using these frameworks, developers can
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Large Language Models (LLMs) like GPT-4 and BERT have become essential tools for natural language processing (NLP) tasks. However, deploying these models in real-time applications, such as chatbots or voice assistants, presents unique challenges. Achieving low-latency inference while maintaining high accuracy is critical. This article will guide you through the
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In the rapidly evolving world of AI, Large Language Models (LLMs) have become increasingly powerful. However, their true potential is unlocked when they can access and integrate information from multiple data sources. This article will guide you through the process of integrating various data sources to enhance LLM retrieval, making
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Large Language Models (LLMs) have revolutionized natural language processing, but their true value lies in their ability to perform in real-world situations. Testing and validating LLMs with realistic scenarios is crucial for ensuring their reliability, safety, and effectiveness. This article will guide you through the process of rigorously evaluating LLMs
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In the world of AI and natural language processing (NLP), large language models (LLMs) like GPT-4 have become indispensable tools for a wide range of applications. However, one of the most challenging aspects of working with these models is controlling their outputs. Whether you're developing a chatbot, content
