title: Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded
    Attributions and Learning to Refuse
summary: 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.
paper_authors._id: 66ea99dec764a0ee6e653ec3
paper_authors.name: Maojia Song
paper_authors._id: 66ea99dec764a0ee6e653ec4
paper_authors.name: Shang Hong Sim
paper_authors._id: 66ea99dec764a0ee6e653ec5
paper_authors.name: Rishabh Bhardwaj
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-19T07:06:58.967Z
paper_authors._id: 5f278507e923d665e616271b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/5f278507e923d665e616271b/tWFuswXOTXtvMdL8zSrr_.png
paper_authors.fullname: Rishabh Bhardwaj
paper_authors.type: user
paper_authors.user: RishabhBhardwaj
paper_authors._id: 66ea99dec764a0ee6e653ec6
paper_authors.name: Hai Leong Chieu
paper_authors._id: 66ea99dec764a0ee6e653ec7
paper_authors.name: Navonil Majumder
paper_authors._id: 66ea99dec764a0ee6e653ec8
paper_authors.name: Soujanya Poria
paper_title: Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded
    Attributions and Learning to Refuse
paper_summary: 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.
paper_ai_keywords: retrieval-augmented generation (RAG) Trust-Score in-context learning Trust-Align LLaMA-3-8b ASQA QAMPARI ELI5