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An adversarial perspective on machine unlearning for ai safety
Large language models are finetuned to refuse questions about hazardous knowledge, but
these protections can often be bypassed. Unlearning methods aim at completely removing …
these protections can often be bypassed. Unlearning methods aim at completely removing …
Open problems in machine unlearning for ai safety
As AI systems become more capable, widely deployed, and increasingly autonomous in
critical areas such as cybersecurity, biological research, and healthcare, ensuring their …
critical areas such as cybersecurity, biological research, and healthcare, ensuring their …
Position: Llm unlearning benchmarks are weak measures of progress
Unlearning methods have the potential to improve the privacy and safety of large language
models (LLMs) by removing sensitive or harmful information post hoc. The LLM unlearning …
models (LLMs) by removing sensitive or harmful information post hoc. The LLM unlearning …
Soft Token Attacks Cannot Reliably Audit Unlearning in Large Language Models
Large language models (LLMs) have become increasingly popular. Their emergent
capabilities can be attributed to their massive training datasets. However, these datasets …
capabilities can be attributed to their massive training datasets. However, these datasets …
A General Framework to Enhance Fine-tuning-based LLM Unlearning
Unlearning has been proposed to remove copyrighted and privacy-sensitive data from
Large Language Models (LLMs). Existing approaches primarily rely on fine-tuning-based …
Large Language Models (LLMs). Existing approaches primarily rely on fine-tuning-based …
Rethinking The Reliability of Representation Engineering in Large Language Models
Z Deng, J Jiang, G Long, C Zhang - openreview.net
Inspired by cognitive neuroscience, representation engineering (RepE) seeks to connect the
neural activities within large language models (LLMs) to their behaviors, providing a …
neural activities within large language models (LLMs) to their behaviors, providing a …