Authors: Yotam Wolf, Noam Wies, Oshri Avnery, Yoav Levine, Amnon Shashua

Published on: April 19, 2023

Impact Score: 8.35

Arxiv code: Arxiv:2304.11082

Summary

• What is new: Introduces the Behavior Expectation Bounds (BEB) framework to analyze alignment in language models.
• Why this is important: Ensuring language models exhibit useful behavior without harm, facing challenges in alignment against adversarial prompts.
• What the research proposes: Presenting a theoretical framework to understand alignment limitations and the susceptibility of models to adversarial prompting.
• Results: Demonstrated that models can be prompted into undesired behaviors if those behaviors are not completely eliminated.

Technical Details

Technological frameworks used: Behavior Expectation Bounds (BEB)

Models used: Large language models (LLMs) such as chatGPT

Data used: Not specified

Potential Impact

AI development companies, platforms utilizing chatbots or language models; potentially impacting user safety and content moderation strategies.

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