LLM tagged posts

Algorithm based on LLMs doubles lossless data compression rates

A powerful lossless data compression algorithm based on LLMs
Image comparing the lossless compression rates of LMCompress with the traditional state-of-the-art methods and the large-model-based method that was proposed independently by a DeepMind-Meta&INRIA team. The comparison is done on four types of data: image, video, audio, and text. It shows that LMCompress consistently outperforms the others on all data types. Note that the DeepMind result on video is not available. Credit: Li et al.

People store large quantities of data in their electronic devices and transfer some of this data to others, whether for professional or personal reasons. Data compression methods are thus of the utmost importance, as they can boost the efficiency of devices and communications, making users less reliant on cloud data services and external storage devices.

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Why GPT cannot think like us

ChatGPT
Credit: Unsplash/CC0 Public Domain

Artificial Intelligence (AI), particularly large language models like GPT-4, has shown impressive performance on reasoning tasks. But does AI truly understand abstract concepts, or is it just mimicking patterns? A new study from the University of Amsterdam and the Santa Fe Institute reveals that while GPT models perform well on some analogy tasks, they fall short when the problems are altered, highlighting key weaknesses in AI’s reasoning capabilities. The work is published in Transactions on Machine Learning Research.

Analogical reasoning is the ability to draw a comparison between two different things based on their similarities in certain aspects...

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LLMs are becoming more Brain-like as they advance, researchers discover

LLM representations mirror human brain responses more closely as LLMs become more advanced
The methodology for predicting brain responses to speech from LLM embeddings, to assess the similarity of various LLMs to the brain. Credit: Gavin Mischler (Figure adapted from Mischler et al., Nature Machine Intelligence, 2024).

Large language models (LLMs), the most renowned of which is ChatGPT, have become increasingly better at processing and generating human language over the past few years. The extent to which these models emulate the neural processes supporting language processing by the human brain, however, has yet to be fully elucidated.

Researchers at Columbia University and Feinstein Institutes for Medical Research Northwell Health recently carried out a study investigating the similarities between LLM representations on neural responses...

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Shrinking AI for Personal Devices: An efficient small language model that could perform better on smartphones

An Android demo of PhoneLM capability. (Left) Chatting; (Right) Device control through intent invocation. Demo and code are available at https://github.com/UbiquitousLearning/mllm. Credit: Yi et al.

Large language models (LLMs), such as Open AI’s renowned conversational platform ChatGPT, have recently become increasingly widespread, with many internet users relying on them to find information quickly and produce texts for various purposes. Yet most of these models perform significantly better on computers, due to the high computational demands associated with their size and data processing capabilities.

To tackle this challenge, computer scientists have also been developing small language models (SLMs), which have a similar architecture but are smaller...

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