Towards trustworthy and reliable language models

Abstract

This thesis addresses the critical challenge of developing trustworthy and reliable Natural Language Processing (NLP) systems, specifically the newly emerged Large Language Models (LLMs). As LLMs become increasingly prevalent in various domains, the need for transparent, interpretable, and controllable AI systems has never been more pressing. However, the complexity of LLMs, the compositional nature of language, and the potential for hallucinations pose significant obstacles to achieving these goals. To increase user trust of AI systems in real-life deployment, we hope to enhance the trustworthiness and reliability of LLMs without requiring model revisions or compromising performance. Motivated by this overarching goal, we delve into two main goals that enhance trustworthiness, providing user-friendly explanations of the LLM’s decisions and controlling the LLM’s behaviors. Specifically, we raise three main research questions: How can we disentangle the true reasons behind LLM decisions from the complex architecture and vast number of parameters? How can we provide user-friendly explanations for LLM generations? How can we increase LLM controllability with minimal interventions?