RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answering
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866914469007851520 |
|---|---|
| author | Wu, Zhuoyu Ou, Wenhui Tan, Pei-Sze Fang, Wenqi Rajanala, Sailaja Phan, Raphaël C. -W. |
| author_facet | Wu, Zhuoyu Ou, Wenhui Tan, Pei-Sze Fang, Wenqi Rajanala, Sailaja Phan, Raphaël C. -W. |
| contents | Retrieving procedure-oriented evidence from materials science papers is difficult because key synthesis details are often scattered across long, context-heavy documents and are not well captured by paragraph-only dense retrieval. We present RECIPER, a dual-view retrieval pipeline that indexes both paragraph-level context and compact large language model-extracted procedural summaries, then combines the two candidate streams with lightweight lexical reranking. Across four dense retrieval backbones, RECIPER consistently improves early-rank retrieval over paragraph-only dense retrieval, achieving average gains of +3.73 in Recall@1, +2.85 in nDCG@10, and +3.13 in MRR. With BGE-large-en-v1.5, it reaches 86.82%, 97.07%, and 97.85% on Recall@1, Recall@5, and Recall@10, respectively. We further observe improved downstream question answering under automatic metrics, suggesting that procedural summaries can serve as a useful complementary retrieval signal for procedure-oriented materials question answering. Code and data are available at https://github.com/ReaganWu/RECIPER. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11229 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answering Wu, Zhuoyu Ou, Wenhui Tan, Pei-Sze Fang, Wenqi Rajanala, Sailaja Phan, Raphaël C. -W. Signal Processing Artificial Intelligence Computation and Language Retrieving procedure-oriented evidence from materials science papers is difficult because key synthesis details are often scattered across long, context-heavy documents and are not well captured by paragraph-only dense retrieval. We present RECIPER, a dual-view retrieval pipeline that indexes both paragraph-level context and compact large language model-extracted procedural summaries, then combines the two candidate streams with lightweight lexical reranking. Across four dense retrieval backbones, RECIPER consistently improves early-rank retrieval over paragraph-only dense retrieval, achieving average gains of +3.73 in Recall@1, +2.85 in nDCG@10, and +3.13 in MRR. With BGE-large-en-v1.5, it reaches 86.82%, 97.07%, and 97.85% on Recall@1, Recall@5, and Recall@10, respectively. We further observe improved downstream question answering under automatic metrics, suggesting that procedural summaries can serve as a useful complementary retrieval signal for procedure-oriented materials question answering. Code and data are available at https://github.com/ReaganWu/RECIPER. |
| title | RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answering |
| topic | Signal Processing Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2604.11229 |