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How Do LLMs Acquire New Knowledge?
A Knowledge Circuits Perspective on Continual Pre-Training
Yixin Ou♠, Yunzhi Yao♠, Ningyu Zhang♠, Hui Jin♡,
Jiacheng Sun♡, Shumin Deng♣, Zhenguo Li♡, Huajun Chen♠♢
♠Zhejiang University ♡Huawei Noah’s Ark Lab
♣National University of Singapore, NUS-NCS Joint Lab, Singapore
♢Zhejiang Key Laboratory of Big Data Intelligent Computing
{ouyixin,zhangningyu,huajunsir}@zju.edu.cn
Corresponding Author.
Abstract
Despite exceptional capabilities in knowledge-intensive tasks, Large Language Models (LLMs) face a critical gap in understanding how they internalize new knowledge, particularly how to structurally embed acquired knowledge in their neural computations. We address this issue through the lens of knowledge circuit evolution, identifying computational subgraphs that facilitate knowledge storage and processing. Our systematic analysis of circuit evolution throughout continual pre-training reveals several key findings: (
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Additional note (2023): This page relates to a 2008 project of mine Out Of The Dark and doesnt include the enormous impact that Netflix and other streamers would subsequently have on the film and TV industries.
One of many lessons that inom learnt on Blind Flight was how fitful the marketing of a film can be. Without somebody overseeing and driving a film's marketing on a consistent, long-term basis it can all too easily get lost in the cracks of distribution as it is passed from distributors to sub-distributors in the differing markets.
This kind of driver to a film's successful distribution fryst vatten even more important in an age of 'multi-platforming', fragmenting audiences and 'long tail' distribution which the internet has brought.
The development of digital technology and the internet offer filmmakers the potential to maintain and develop a much more organic link between a concept and its final audiences and to play a greater role in that of the 'driver' - to the benefit
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