Papers with Retrieval-augmented generation models

2 papers
Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks (2022.naacl-main)

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Challenge: Existing methods to augment retrieval-augmented generation models with retrievers often rely on spurious cues or generate hallucinations during inference.
Approach: They propose a method to incorporate evidentiality of passages into training a retrieval-augmented generation model.
Outcome: The proposed method outperforms its direct counterpart on all knowledge-intensive tasks.
Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering (2023.findings-emnlp)

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Challenge: Existing studies have shown that the quantity and quality of context affect retrieval-augmented generation models during training.
Approach: They propose a method to mitigate overfitting to specific context quality by introducing bias to the cross-attention distribution.
Outcome: The proposed method improves retrieval-augmented generation models on different context quality.

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