Beyond Independent Passages: Adaptive Passage Combination Retrieval for Retrieval Augmented Open-Domain Question Answering
Ting-Wen Ko, Jyun-Yu Jiang, Pu-Jen Cheng

TL;DR
This paper introduces AdaPCR, a novel retrieval framework that models dependencies between passages to improve open-domain question answering, especially for multi-hop questions, by adaptively selecting passage combinations for better context and answer accuracy.
Contribution
AdaPCR explicitly models inter-passage dependencies and adaptively retrieves passage combinations, advancing retrieval-augmented generation for open-domain QA.
Findings
Outperforms baseline methods on multiple QA benchmarks.
Significantly improves multi-hop reasoning accuracy.
Effectively models passage dependencies for better retrieval.
Abstract
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external documents at inference time, enabling up-to-date knowledge access without costly retraining. However, conventional RAG methods retrieve passages independently, often leading to redundant, noisy, or insufficiently diverse context-particularly problematic - particularly problematic in noisy corpora and for multi-hop questions. To address this, we propose Adaptive Passage Combination Retrieval (AdaPCR), a novel framework for open-domain question answering with black-box LMs. AdaPCR explicitly models dependencies between passages by considering passage combinations as units for retrieval and reranking. It consists of a context-aware query reformulation using concatenated passages, and a reranking step trained with a predictive objective aligned with downstream answer likelihood. Crucially,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
