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.
66ea99dec764a0ee6e653ec3
Maojia Song
66ea99dec764a0ee6e653ec4
Shang Hong Sim
66ea99dec764a0ee6e653ec5
Rishabh Bhardwaj
claimed_verified
2024-09-19T07:06:58.967Z
5f278507e923d665e616271b
https://cdn-avatars.huggingface.co/v1/production/uploads/5f278507e923d665e616271b/tWFuswXOTXtvMdL8zSrr_.png
Rishabh Bhardwaj
user
RishabhBhardwaj
66ea99dec764a0ee6e653ec6
Hai Leong Chieu
66ea99dec764a0ee6e653ec7
Navonil Majumder
66ea99dec764a0ee6e653ec8
Soujanya Poria
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.
Trust-Score and Trust-Align framework improve the trustworthiness and performance of LLMs in retrieval-augmented generation tasks.
LLMs retrieval-augmented generation Trust-Score in-context learning Trust-Align ASQA QAMPARI ELI5