Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse
<|reference_start|>Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse: 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.<|reference_end|>
@article{song2024measuring,
    title={Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded
  Attributions and Learning to Refuse},
    author={Maojia Song, Shang Hong Sim, Rishabh Bhardwaj, Hai Leong Chieu,
  Navonil Majumder, Soujanya Poria},
    journal={arXiv preprint arXiv:2409.11242},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11242},
    primaryClass={cs.CL}
}