QUBO-Optimized Evidence Selection for Retrieval-Augmented Question Answering with Unconventional Solvers
Retrieval-augmented question answering depends on selecting evidence passages that jointly support answer generation. However, many RAG pipelines rely on top-\(k\) ranking, where passages are selected mainly by individual relevance scores, even though multi-hop questions often require complementary evidence satisfying multiple information requirements. Recent LLM-based selectors address this by treating retrieval as set selection, but using an LLM for this intermediate stage can be costly and difficult to scale. In this work, we formulate evidence selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Given a question, candidate passages, and decomposed information req...
| Authors | Rahul Singh, Madhav Vadlamani |
|---|---|
| Primary category | cs.CL |
| Published | 14 July 2026 |
| Last updated at source | 14 July 2026 |
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