This week, we will walk through and discuss the paper: How much do language models memorize?
[[https://arxiv.org/pdf/2505.24832](https://arxiv.org/pdf/2505.24832 "https://arxiv.org/pdf/2505.24832")]

Abstract of the paper:
We propose a new method for estimating how much a model \`\`knows'' about a datapoint and use it to measure the capacity of modern language models. Prior studies of language model memorization have struggled to disentangle memorization from generalization. We formally separate memorization into two components: \textit{unintended memorization}, the information a model contains about a specific dataset, and \textit{generalization}, the information a model contains about the true data-generation process. When we completely eliminate generalization, we can compute the total memorization, which provides an estimate of model capacity: our measurements estimate that GPT-style models have a capacity of approximately 3.6 bits per parameter. We train language models on datasets of increasing size and observe that models memorize until their capacity fills, at which point \`\`grokking'' begins, and unintended memorization decreases as models begin to generalize. We train hundreds of transformer language models ranging from 500K to 1.5B parameters and produce a series of scaling laws relating model capacity and data size to membership inference.

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-

We are a group of applied AI practitioners and enthusiasts who have formed a collective learning community. Every Wednesday evening at PM PST, we hold our research paper reading seminar covering an AI topic. One member carefully explains the paper, making it more accessible to a broader audience. Then, we follow this reading with a more informal discussion and socializing.

You are welcome to join this in person or over Zoom. **[SupportVectors](https://supportvectors.io)**[ ](https://supportvectors.io)is an AI training lab located in Fremont, CA, close to Tesla and easily accessible by road and BART. We follow the weekly sessions with snacks, soft drinks, and informal discussions.

If you want to attend by Zoom, the Zoom registration link will be visible once you RSVP. Note that we have had to change and add security to the Zoom link to prevent Zoom bombing.

SupportVectors: Generative AI, LLMs, Machine Learning

Kate Amon

SupportVectors AI Labs

Henry Kendall

Shan

Venkata Cherukuri

Bobby

Mike Hillowalla

Shamshad

Eugene

Jvs Mani

Karan

Shubhankar Tripathy

Sylvia

arsheenkhatib94

Maryam Saberi

Walter Heger

Jahnavi Ravi

Rahul Bagga

Fahimeh Sharafkhani

yixuanjing116

[Paper Reading]: How much do language models memorize?

SupportVectors

{networkName}を検索する

グループ

{PRO_NETWORK_NAME}はMeetup Proに登録しており、世界中の{PRO_GROUP_COUNT}のMeetupで{PRO_MEMBER_COUNT}人以上のメンバーが参加しています。Meetup Proは、ユーザー、パートナー、コントリビューター、メンバーのネットワークを管理できるプロフェッショナルなコミュニケーションツールです。

SupportVectors

SupportVectors

私たちについて

SupportVectorsを検索する

今後のイベント

グループ