Demystifying Embedding Spaces using Large Language Models;Guy Tennenholtz et al An Emulator for Fine-Tuning Large Language Models using Small Language Models;Eric Mitchell et al UNVEILING A CORE LINGUISTIC REGION IN LARGE LANGUAGE MODELS;Jun Zhao et al DETECTING PRETRAINING DATA FROM LARGE LANGUAGE M...
We present statistical analyses of the large-scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path lengths between words, and strong local ...
Word Tokenization: Splits the text into words based on spaces or punctuation marks. Example: “I love coding” → [“I”, “love”, “coding”] Sub-word Tokenization: Breaks down words into smaller meaningful units. Example: “unhappiness” → [“un”, “happiness”] ...
If the structure of language vocabularies mirrors the structure of natural divisions that are universally perceived, then the meanings of words in different languages should closely align. By contrast, if shared word meanings are a product of shared cult
The advent of large language models (LLMs) has marked a new era in the transformation of computational social science (CSS). This paper dives into the role of LLMs in CSS, particularly exploring their potential to revolutionize data analysis and content generation and contribute to a broader un...
- 然鹅,市场上已经出现4款模型,实现了对OpenAI 的超越,其中两款基于目前最火的开源模型 Mistral。榜单网址:链接。 #Embedding #word embedding #词向量 #rag #OpenAI #huggingface #AI #人工智能 #深度学习 深度学习(Deep Learning) #大模型 大语言模型 #Mistral...
2.4.1. The relationship between data volume and model capacity To ensure optimal performance in the face of increasing training costs for deep learning and large language models, we investigate the relationship between data volume and model capacity, specifically, the neural scaling laws. These laws...
Open-source LLMs: TheHugging Face Hubis a great place to find LLMs. You can directly run some of them inHugging Face Spaces, or download and run them locally in apps likeLM Studioor through the CLI withllama.cpporOllama. Prompt engineering: Common techniques include zero-shot prompting, fe...
After inspecting the most highlighted sentences from a broader perspective, we identified the most frequently occurring words from the top-attended sentences across all reports. We quantified this by taking the ratio between the number of occurrences of a given word in the most highly attended senten...
One possible source of this myth is a change in the school systems between countries. When Einstein took an entrance exam for the Swiss Federal Polytechnic School (later the ETH Zurich) at the age of 16, he excelled in the mathematics and physics sections but did not do as well in the ...