Learning and Forgetting Unsafe Examples in Large Language Models

We explore the behavior of LLMs finetuned on noisy custom data containing unsafe content, represented by datasets that contain biases, toxicity, and ...

The study finds that LLMs can learn unsafe content but tend to forget it when subsequently trained on safer data. To address this, the " ...

Learning and Forgetting Unsafe Examples in Large Language Models

Learning and Forgetting Unsafe Examples in Large Language Models. Jiachen Zhao1, Zhun Deng2, David Madras3, James Zou4, and Mengye Ren5.

Learning and Forgetting Unsafe Examples in Large Language Models

code for the paper ``Learning and Forgetting Unsafe Examples in Large Language Models'', accepted by ICML 24 - andotalao24/learn-forget-unsafe-llm.

Learning and Forgetting Unsafe Examples in Large Language Models

This work introduces the ForgetFilter algorithm, which filters unsafe data based on how strong the model's forgetting signal is for that data, ...

Learning and Forgetting Unsafe Examples in Large Language Models

This paper investigates how LLMs can learn and then "forget" unsafe examples during the continuous fine-tuning process. The researchers designed ...

Teaching large language models to “forget” unwanted content - IBM

Using machine unlearning on a Llama model, for instance, Baracaldo's team at IBM was able to reduce the toxicity score from 15.4% toxicity to ...

Mitigating Catastrophic Forgetting in Large Language Models with ...

We propose the Self-Synthesized Rehearsal. (SSR) framework to mitigate catastrophic forget- ting in continual learning. As shown in Figure 1,.

Deep Forgetting & Unlearning for Safely-Scoped LLMs

However, large pretrained language models tend to be very resistant to forgetting (Ramasesh et al., 2022; Cossu et al., 2022; Li et al ...

This AI Paper Introduces the 'ForgetFilter': A Machine Learning ...

A pressing concern has surfaced in large language models (LLMs), drawing attention to the safety implications of downstream customized ...

Deep Forgetting & Unlearning for Safely-Scoped LLMs - LessWrong

These can either involve passively forgetting out-of-distribution knowledge or actively unlearning knowledge in some specific undesirable domain ...

Learning and Forgetting Unsafe Examples in Large Language Models

As the number of large language models (LLMs) released to the public grows, there is a pressing need to understand the safety implications associated with ...

Learning and Forgetting Unsafe Examples in Large Language Models

我们发现，即使与安全数据对齐的LLM可以轻易地学习这些不安全内容，但在后续在安全数据上微调时，它们也倾向于忘记它更显著。从忘记的差异中得到启示，我们 ...

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Learning a Diffusion Model Policy from Rewards via Q-Score Matching · Learning and Forgetting Unsafe Examples in Large Language Models · Learning Associative ...

[D] LLMs are known for catastrophic forgetting during continual fine ...

But how is Chatgpt-4 able to remember all the factual data that it learned? In other words, how can LLMs remember the data that they learned ...

Google at ICML 2024

Learning and Forgetting Unsafe Examples in Large Language Models Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren. A Near-Linear ...

Digital Forgetting in Large Language Models: A Survey of ... - Synthical

The objective of digital forgetting is, given a model with undesirable knowledge or behavior, obtain a new model where the detected issues ...

Machine unlearning for LLMs - IBM Research

A new field called large language model unlearning is centered on removing the influence of unwanted data on a trained LLM.

Investigating the Catastrophic Forgetting in Multimodal Large ...

hancing vision-language understanding with advanced large language models. ... and learn: Fine-tuning deep pretrained language models with less forgetting.

Learning and Forgetting Unsafe Examples in Large Language Models

随着向公众发布的大型语言模型(LLM) 数量的增长，迫切需要了解与从第三方自定义微调数据学习的这些模型相关的安全影响。我们探索了对 ...