Tag

#language models

4 articles

Even frontier LLMs from GPT-5 onward lose up to 33% accuracy when you chat too long

Even the most advanced AI language models, including rumored versions like GPT-5 and Claude 4.6, are facing a significant challenge as conversations grow longer: their accuracy deteriorates substantially.

Feb 287

Current language model training leaves large parts of the internet on the table

A new study reveals that the tools used to extract web content for training large language models can significantly impact which parts of the internet are included in AI datasets. This inconsistency raises concerns about the representativeness and fairness of AI training data.

Feb 2810

Liquid AI’s New LFM2-24B-A2B Hybrid Architecture Blends Attention with Convolutions to Solve the Scaling Bottlenecks of Modern LLMs

Learn to build a hybrid neural network architecture that combines attention mechanisms with convolutional layers, similar to Liquid AI's LFM2-24B-A2B model, to address scaling bottlenecks in large language models.

Feb 2515

RAG vs. Context Stuffing: Why selective retrieval is more efficient and reliable than dumping all data into the prompt

As language models gain the ability to process massive context windows, experts argue that selective retrieval methods like RAG remain more efficient and reliable than simply dumping all data into prompts.

Feb 236