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This content will become publicly available on July 17, 2026

Title: ROSERAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization
Large language models (LLMs) have achieved impressive performance but face high computational costs and latency, limiting their deployment in resource-constrained settings. In contrast, small-scale LLMs (SLMs) are more efficient yet struggle to capture evolving real-world knowledge. Retrieval-augmented generation (RAG) helps by integrating external knowledge, but imperfect retrieval can introduce distracting noise that misleads SLMs. We propose {\name}, a robust RAG framework for SLMs via Margin-aware Preference Optimization. {\name} employs multi-turn prompting for detailed reasoning, rejection sampling for high-quality explanations, and contrastive preference selection to refine responses by maximizing the likelihood gap between preferred and non-preferred outputs.  more » « less
Award ID(s):
2340241
PAR ID:
10600833
Author(s) / Creator(s):
; ; ; ; ; ; ;
Publisher / Repository:
ACL Findings
Date Published:
Journal Name:
Proceedings of Machine Learning Research
ISSN:
0736-587X
ISBN:
9798331301491
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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