Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded
  Attributions and Learning to Refuse
Authors: name: Maojia Song, name: Shang Hong Sim, name: Rishabh Bhardwaj, name: Hai Leong Chieu, name: Navonil Majumder, name: Soujanya Poria

LLMs are an integral part of retrieval-augmented generation (RAG) systems.
While many studies focus on evaluating the quality of end-to-end RAG systems,
there is a lack of research on understanding the appropriateness of an LLM for
the RAG task. Thus, we introduce a new metric, Trust-Score, that provides a
holistic evaluation of the trustworthiness of LLMs in an RAG framework. We show
that various prompting methods, such as in-context learning, fail to adapt LLMs
effectively to the RAG task. Thus, we propose Trust-Align, a framework to align
LLMs for higher Trust-Score. LLaMA-3-8b, aligned with our method, significantly
outperforms open-source LLMs of comparable sizes on ASQA (up 10.7), QAMPARI (up
29.2) and ELI5 (up 14.9). We release our code at:
https://github.com/declare-lab/trust-align.