Model Behavior

Research on large language model (LLM) behavior focuses on understanding and mitigating undesirable outputs, such as toxicity or biases, while improving desirable traits like helpfulness and accuracy. Current efforts investigate methods for post-hoc safety alignment, analyzing the impact of decoding strategies and persona assignment on model responses, and developing techniques to interpret and edit model behavior through targeted interventions or data manipulation. These studies are crucial for building safer and more reliable LLMs, impacting both the development of trustworthy AI systems and the advancement of our understanding of complex model architectures.