LLM Fragility: Exploring Set Membership Vulnerabilities

This ArXiv paper likely delves into the weaknesses of Large Language Models (LLMs) when dealing with set membership tasks, exposing potential vulnerabilities. The study's focus on set membership provides valuable insights into LLMs' limitations, potentially informing future research on robustness.

• •The paper likely identifies scenarios where LLMs fail to correctly identify set membership.
• •The research probably highlights specific weaknesses related to the LLMs internal representations.
• •The findings likely contribute to the broader understanding of LLM limitations and potential biases.