LLM

We study test-time methods for self‑improving and self‑adapting agents which represent an emerging frontier of artificial intelligence in which autonomous systems can not only perform tasks but also learn from interactions, refine their own behaviors, and even modify their own code or learning processes over time.

We incoporate external knowledge and goals for better CRS.

We generate high-quality code-mixed sentences using language models.

We find positive use cases, but also weaknesses that need much handholding from human scientists.

We measure and enhance LLMs’ discourse understanding and reasoning faithfulness.

We provide language resource for emoji composition.

This project develops a visualizer utilising attention scores to enhance transformer interpretability.