arXiv:2606.00432v1 Announce Type: new Abstract: As retrieval-augmented generation (RAG) systems scale, it becomes increasingly challenging to ensure faithful grounding in external evidence. Large language models may still prioritize parametric knowledge over retrieved information when conflicts arise. We propose a novel training-free decoding framework, \emph{Grounded Decoding}, designed to improve factual consistency in RAG without modifying model parameters. Unlike standard approaches that rely on a single conditional distribution, our method constructs two matched-prompt distributions at ev
The increasing scale and deployment of RAG systems highlight persistent issues with factual consistency, driving immediate research into fidelity improvements.
Improving the faithfulness of RAG systems is critical for their reliability in high-stakes applications and for preventing the spread of misinformation from AI.
Decoding frameworks for RAG systems can now be enhanced without needing to retrain large language models, offering a more agile path to improved factual grounding.
- · AI developers
- · RAG system users
- · Ethical AI advocates
- · Generative AI systems prone to hallucination
- · Users relying on ungrounded AI outputs
RAG systems will become more trustworthy, leading to broader adoption in critical sectors.
Increased trust in AI outputs may accelerate automation of information-intensive tasks, impacting white-collar work.
As AI-generated content becomes more reliable, discerning human-generated versus AI-generated factual information may become harder.
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Read at arXiv cs.LG