@inproceedings{gu-etal-2025-toward,
title = "Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience",
author = "Gu, Jiawei and
Xian, Ziting and
Xie, Yuanzhen and
Liu, Ye and
Liu, Enjie and
Zhong, Ruichao and
Gao, Mochi and
Tan, Yunzhi and
Hu, Bo and
Li, Zang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1224/",
doi = "10.18653/v1/2025.findings-acl.1224",
pages = "23891--23910",
ISBN = "979-8-89176-256-5",
abstract = "Large language models (LLMs) achieve strong performance on plain text tasks but underperform on structured data like tables and databases. Potential challenges arise from their underexposure during pre-training and rigid text-to-structure transfer mechanisms. Unlike humans who seamlessly apply learned patterns across data modalities, LLMs struggle to infer implicit relationships embedded in tabular formats, especially in the absence of explicit structural guidance. To bridge this cognitive gap, we introduce Contrastive Retrieval-Augmented Generation on Experience (CoRE), a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning (ICL) to simulate human-like knowledge transfer. Experiments on Text-to-SQL and TableQA show CoRE significantly improves performance, achieving average gains of 3.44{\%} and 4.24{\%}, with up to 17.2{\%} on challenging tasks. Our Monte Carlo Tree Search (MCTS)-generated Experience Memory expands training data 8-9{\texttimes}, enhancing diversity and domain coverage. This training-free and continual method propels LLMs toward structured knowledge expertise."
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<abstract>Large language models (LLMs) achieve strong performance on plain text tasks but underperform on structured data like tables and databases. Potential challenges arise from their underexposure during pre-training and rigid text-to-structure transfer mechanisms. Unlike humans who seamlessly apply learned patterns across data modalities, LLMs struggle to infer implicit relationships embedded in tabular formats, especially in the absence of explicit structural guidance. To bridge this cognitive gap, we introduce Contrastive Retrieval-Augmented Generation on Experience (CoRE), a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning (ICL) to simulate human-like knowledge transfer. Experiments on Text-to-SQL and TableQA show CoRE significantly improves performance, achieving average gains of 3.44% and 4.24%, with up to 17.2% on challenging tasks. Our Monte Carlo Tree Search (MCTS)-generated Experience Memory expands training data 8-9×, enhancing diversity and domain coverage. This training-free and continual method propels LLMs toward structured knowledge expertise.</abstract>
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%0 Conference Proceedings
%T Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience
%A Gu, Jiawei
%A Xian, Ziting
%A Xie, Yuanzhen
%A Liu, Ye
%A Liu, Enjie
%A Zhong, Ruichao
%A Gao, Mochi
%A Tan, Yunzhi
%A Hu, Bo
%A Li, Zang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F gu-etal-2025-toward
%X Large language models (LLMs) achieve strong performance on plain text tasks but underperform on structured data like tables and databases. Potential challenges arise from their underexposure during pre-training and rigid text-to-structure transfer mechanisms. Unlike humans who seamlessly apply learned patterns across data modalities, LLMs struggle to infer implicit relationships embedded in tabular formats, especially in the absence of explicit structural guidance. To bridge this cognitive gap, we introduce Contrastive Retrieval-Augmented Generation on Experience (CoRE), a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning (ICL) to simulate human-like knowledge transfer. Experiments on Text-to-SQL and TableQA show CoRE significantly improves performance, achieving average gains of 3.44% and 4.24%, with up to 17.2% on challenging tasks. Our Monte Carlo Tree Search (MCTS)-generated Experience Memory expands training data 8-9×, enhancing diversity and domain coverage. This training-free and continual method propels LLMs toward structured knowledge expertise.
%R 10.18653/v1/2025.findings-acl.1224
%U https://aclanthology.org/2025.findings-acl.1224/
%U https://doi.org/10.18653/v1/2025.findings-acl.1224
%P 23891-23910
Markdown (Informal)
[Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience](https://aclanthology.org/2025.findings-acl.1224/) (Gu et al., Findings 2025)
ACL
- Jiawei Gu, Ziting Xian, Yuanzhen Xie, Ye Liu, Enjie Liu, Ruichao Zhong, Mochi Gao, Yunzhi Tan, Bo Hu, and Zang Li. 2025. Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience. In Findings of the Association for Computational Linguistics: ACL 2025, pages 23891–23910, Vienna, Austria. Association for Computational Linguistics.