Large Language Models (LLMs) generate grounded answers using Retrieval-Augmented Generation (RAG), reducing hallucinations. Prior work has focused on answer and attribution correctness, with recent studies exploring faithfulness through internal signals that reflect the model’s decision process. However, these approaches often introduce latency and do not tightly align token generation with attribution. We propose EARAL, a novel attribution method for RAG that improves faithfulness through joint answer generation and generation-time attribution with minimal overhead. Documents are marked with special token identifiers, whose logits estimate their contribution during generation and are aggregated into document-level attributions. On the Trust-Align benchmark for trustworthy attributed generation, EARAL outperforms state-of-the-art methods across metrics. Additional analysis shows low latency, robustness, and strong alignment with faithfulness.

Passage retrieval is crucial in specialized domains where documents are long and complex, such as patents, legal documents, scientific reports, etc. We explore in this paper the integration of Entities and passages in Heterogeneous Attention Graph Models dedicated to passage retrieval. We use the two passage retrieval architectures based on re-ranking proposed in [1]. We experiment our proposal on the TREC CAR Y3 Passage Retrieval Task. The results obtained show an improvement over state-of-the-art techniques and proves the effectiveness of the approach. Our experiments also show the importance of using adequate parameters for such approach.