ARK: Answer-Centric Retriever Tuning via KG-augmented Curriculum Learning

• URL: http://arxiv.org/abs/2511.16326v1
• Date: Thu, 20 Nov 2025 13:05:09 GMT
• Title: ARK: Answer-Centric Retriever Tuning via KG-augmented Curriculum Learning
• Authors: Jiawei Zhou, Hang Ding, Haiyun Jiang,
• Abstract summary: We propose a novel fine-tuning framework that optimize the retriever for Answer Alignment.<n>We first identify high-quality positive chunks by evaluating their sufficiency to generate the correct answer.<n>We then employ a curriculum-based contrastive learning scheme to fine-tune the retriever.
• Score: 17.026973494557303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) has emerged as a powerful framework for knowledge-intensive tasks, yet its effectiveness in long-context scenarios is often bottlenecked by the retriever's inability to distinguish sparse yet crucial evidence. Standard retrievers, optimized for query-document similarity, frequently fail to align with the downstream goal of generating a precise answer. To bridge this gap, we propose a novel fine-tuning framework that optimizes the retriever for Answer Alignment. Specifically, we first identify high-quality positive chunks by evaluating their sufficiency to generate the correct answer. We then employ a curriculum-based contrastive learning scheme to fine-tune the retriever. This curriculum leverages LLM-constructed Knowledge Graphs (KGs) to generate augmented queries, which in turn mine progressively challenging hard negatives. This process trains the retriever to distinguish the answer-sufficient positive chunks from these nuanced distractors, enhancing its generalization. Extensive experiments on 10 datasets from the Ultradomain and LongBench benchmarks demonstrate that our fine-tuned retriever achieves state-of-the-art performance, improving 14.5% over the base model without substantial architectural modifications and maintaining strong efficiency for long-context RAG. Our work presents a robust and effective methodology for building truly answer-centric retrievers.

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