LLM Robustness

Large language model (LLM) robustness research focuses on improving the reliability and safety of LLMs by mitigating vulnerabilities to adversarial attacks, such as "jailbreaks" that elicit harmful outputs, and ensuring consistent performance across diverse inputs and tasks. Current research explores various defense mechanisms, including adversarial training techniques (like refusal feature adversarial training), data curation methods to remove harmful content from training data, and meta-training approaches to improve generalization and in-context learning. This work is crucial for building trustworthy and dependable LLMs, enabling their safe and effective deployment in real-world applications while addressing concerns about bias, misinformation, and malicious use.