💬 Natural Language Processing - hello · Scour

The Evolution of Natural Language Processing: A Journey from 1960 to 2020 📝NLP

dev.to·19h·DEV·

LLM Tokenizers Simplified: BPE, SentencePiece, and More 📝Parsing

digitalocean.com·3d·

\textit{Versteasch du mi?} Computational and Socio-Linguistic Perspectives on GenAI, LLMs, and Non-Standard Language 📝Parsing

arxiv.org·2d·

Improving few-shot named entity recognition for large language models using structured dynamic prompting with retrieval augmented generation 🔍RAG

nature.com·2d·

Use of Large Language Models in Standards Work 📝Parsing

w3.org·6d·Hacker News·

Inference Engines — A visual deep dive into the journey of a token down the transformer layers 📱Edge AI

femiadeniran.com·4d·r/LocalLLaMA·

LLMs and Agents: How do they Work? 💬Prompt Engineering

mattrogish.com·5d·Hacker News·

WARP: Guaranteed Inner-Layer Repair of NLP Transformers 🤖Transformers

arxiv.org·20h·

Before LLMs Could Predict, They Had to Count 📝NLP

dev.to·1d·DEV·

Variational Neurons in Transformers for Language Modeling 🤖Transformers

arxiv.org·2d·

Scaling LLMs at the Edge: A journey through distillation, routers, and embeddings 🦙Ollama

dev.to·1d·DEV·

Drift-Aware Continual Tokenization for Generative Recommendation 📊HyperLogLog

arxiv.org·1d·

A Review on Language Models as Knowledge Bases 📝Parsing

dev.to

·4d·DEV·

Conversational Agents and the Understanding of Human Language: Reflections on AI, LLMs, and Cognitive Science 📝Parsing

arxiv.org·2d·

Performance Evaluation of LLMs in Automated RDF Knowledge Graph Generation 🕸️Knowledge Graphs

arxiv.org·1d·

On the Expressive Power of Contextual Relations in Transformers 🌳Pratt Parsing

arxiv.org·3d·

Concept Training for Human-Aligned Language Models 🤖Transformers

arxiv.org·1d·

Approaches to Analysing Historical Newspapers Using LLMs 📝NLP

arxiv.org·6d·

Toward Culturally Grounded Natural Language Processing 📝Parsing

arxiv.org·3d·

Sparse Auto-Encoders and Holism about Large Language Models 📝Parsing

arxiv.org·3d·

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