Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse

• URL: http://arxiv.org/abs/2409.11242v2
• Date: Fri, 11 Oct 2024 08:53:31 GMT
• Title: Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse
• Authors: Maojia Song, Shang Hong Sim, Rishabh Bhardwaj, Hai Leong Chieu, Navonil Majumder, Soujanya Poria,
• Abstract summary: We introduce Trust-Score, a holistic metric that evaluates the trustworthiness of LLMs within the RAG framework. Our results show that various prompting methods, such as in-context learning, fail to effectively adapt LLMs to the RAG task. We propose Trust-Align, a method to align LLMs for improved Trust-Score performance.
• Score: 27.26121507279163
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLMs are an integral component of retrieval-augmented generation (RAG) systems. While many studies focus on evaluating the overall quality of end-to-end RAG systems, there is a gap in understanding the appropriateness of LLMs for the RAG task. To address this, we introduce Trust-Score, a holistic metric that evaluates the trustworthiness of LLMs within the RAG framework. Our results show that various prompting methods, such as in-context learning, fail to effectively adapt LLMs to the RAG task as measured by Trust-Score. Consequently, we propose Trust-Align, a method to align LLMs for improved Trust-Score performance. The LLaMA-3 family, aligned using our method, significantly outperforms open-source LLMs of similar sizes on ASQA (up 14.0), QAMPARI (up 28.9), and ELI5 (up 13.7). We also demonstrate the effectiveness of Trust-Align across different open-weight models, including the LLaMA series (1b to 8b), Qwen-2.5 series (0.5b to 7b), and Phi3.5 (3.8b). We release our code at \url{https://anonymous.4open.science/r/trust-align}

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