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20242026
most citedMeasuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse

2 citations · 3 across the 4 of their papers we have counts for

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cs.CL2025

Lessons from Training Grounded LLMs with Verifiable Rewards

Shang Hong Sim, Tej Deep Pala, Vernon Toh +5

Generating grounded and trustworthy responses remains a key challenge for large language models (LLMs). While retrieval-augmented generation (RAG) with citation-based grounding hol…

cs.CL2024★ 2 cited

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

Maojia Song, Shang Hong Sim, Rishabh Bhardwaj +3

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…