Investigating methods to align chain-of-thought reasoning in LLMs with human logical processes, focusing on faithfulness and transparency of intermediate steps.

Developing efficient transfer learning techniques that enable NLP capabilities in languages with limited training data, leveraging structural similarities across language families.

Designing feedback interfaces that allow non-expert users to iteratively improve model behavior through natural interaction patterns.

Building a systematic evaluation framework that tests foundation models across multiple safety dimensions including toxicity, bias, and adversarial robustness.