The following focuses on the basic idea of hint engineering and how it can improve the performance of the Large Language Model (LLM)... Interfaces for LLM: One of the key reasons why large language models are so hot is that their text-to-text interfaces enable a minimalist operational experience. In the past, solving tasks using deep learning typically required...
Open source address: https://github.com/cpacker/MemGPT Thesis address: https://arxiv.org/abs/2310.08560 Official website: https://memgpt.ai/ MemGPT supports: 1. Management of long-term memory or state 2. Linking of RAG-based technologies External data sources 3.
This site recommends many based on oneapi/newapi paid and free transit API, some unscrupulous service providers on the model miserable false, we use a variety of verification methods, review the model authenticity, available models, response time. The result is for reference only, to prevent the gentleman not to prevent the villain. (Only verify the domestic accessible API, the KEY you submit in the local storage does not leak)
This beginner's guide consists of seven chapters that contain everything you need to understand the basics of SEO and start improving your rankings. You'll also find links to helpful resources on our SEO blog and YouTube channel so you can build your own path to SEO savvy . 1/ How Search Engines Work...
Original article: https://www.hbs.edu/ris/PublicationFiles/24-013_d9b45b68-9e74-42d6-a1c6-c72fb70c7282.pdf The purpose of this paper is to explore the impact of artificial intelligence on the productivity and quality of knowledge workers, with field experiments Drawing conclusions. The research team includes researchers from ha...
Original: https://arxiv.org/pdf/2210.03629.pdf Can't understand how ReAct works and applies even after reading it? See ReAct Implementation Logic in Practice with real-world examples. Abstract Although large-scale language models (LLMs) are useful in the tasks of language understanding and interactive decision...
RAG (Retrieve Augmented Generation) is a technique for optimizing the output of Large Language Models (LLMs) based on authoritative knowledge base information. This technique extends the functionality of LLMs to refer to the internal knowledge base of a particular domain or organization when generating responses to...
Original text: "Dense X Retrieval: What Retrieval Granularity Should We Use?" Note: This method is suitable for a small number of models, such as the OPENAI series, the Claude series, Mixtral, Yi, and qwen. Abstract In open-domain natural language processing (NLP) tasks, ...
Today I read an interesting paper "Large Language Models as Analogical Reasoners", which mentions a new approach to Prompt - "Analogical Prompting. If you are familiar with prompt engineering, you must have heard of "Chain of Thought" (CoT), which is an analogical method of prompting...
Original: Generally Capable Agents in Open-Ended Worlds [S62816] 1. Reflective Intelligence Able to check and modify the code or content it generates, and optimize iteratively Through self-reflection and revision, it can generate higher quality results It is a robust and effective technique...
Abstract The reasoning performance of Large Language Models (LLMs) on a wide range of problems relies heavily on chained-thinking prompts, which involves providing a number of chained-thinking demonstrations as exemplars in the prompts. Recent research, e.g., thinking trees, has pointed to exploration and self-assessment of reasoning in complex problem solving ...
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