UC San Diego

In the rapidly evolving field of Natural Language Processing (NLP), the advent of Large Language Models (LLMs) marks a significant milestone, setting new standards in language understanding and generation. This thesis focuses on augmenting and evaluating LLMs, introducing ToolkenGPT, a novel method to integrate external tools via tool embeddings to enrich model functionality and adaptability and RepoBench, a benchmark for assessing the proficiency of LLMs in handling repository-level code auto-completion. Additionally, this thesis rethinks approaches towards tabular data reasoning, exploring how LLMs can be better tailored to understand and interpret structured data formats effectively.