Elamur Research⌕
☰
home / papers / 2025 / Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning

Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning

Paper · 2025 · Yongxin Xu, Ruizhe Zhang, Xinke Jiang et al.

Summary

Retrieval-augmented LLMs face two competing demands. They should trust retrieved evidence even when it contradicts what they memorized (adherence), and they should ignore retrieved passages that look relevant but are actually noise (robustness). Training aimed at one of these tends to hurt the other. The authors propose Parenting, a framework inspired by how the brain localizes functions to specific regions.

First, a probing dataset is used to score each parameter unit (one weight matrix) for its importance to each behaviour. The score combines forward-pass activations with gradient signals from backpropagation. This splits the model into three parts:

  • an adherence-specific subspace,
  • a robustness-specific subspace,
  • an entangled subspace that serves both.

Second, each subspace is tuned differently. The entangled parameters are trained on an additional document-extraction task. Boundary-controlled fine-tuning keeps conflicting supervision from leaking into the two specific subspaces.

Training data was built from SQuAD2.0. Experiments across several models and datasets show balanced gains on both behaviours and better handling of out-of-distribution data. This points to parameter-level control as a practical route to more reliable RAG.

Key points

  • On noise identification with a LLaMA2-7B-Chat backbone, Parenting reached 69.89% accuracy on SQuAD and 72.50% on RGB. The comparison systems scored as follows:

    SystemSQuADRGB
    KAFT59.24%65.00%
    RAAT62.48%67.00%
    Base model55.49%63.50%

  • The target behaviour is explicit. When the gold evidence is present, the model should answer from the context, even if it conflicts with its memory. When only irrelevant context is retrieved, it should fall back on its own parametric knowledge rather than refuse.

  • In LLaMA2-7B-Chat, the subspaces sit at different depths:

    • adherence-specific units are mostly in the middle and upper-middle layers,
    • robustness-specific units are mainly in the upper layers,
    • entangled units are concentrated in the middle to lower-middle layers.

  • The authors list several limitations:

    • The remaining errors cluster on the implicit-reasoning KNOT-I subset of the KNOT dataset.
    • Training data comes only from SQuAD2.0.
    • Matrix-level units may be too coarse, and LoRA-based finer units are suggested as future work.

  • The code is released at github.com/Nostradamus4869/Parenting.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Peking UniversityBeijing · China244 works · 39,359 citations🎓Hong Kong Polytechnic UniversityHong Kong · China84 works · 4,956 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksInformation retrieval & search · 473 works

Cite

Cite as Yongxin Xu, Ruizhe Zhang, Xinke Jiang, Yujie Feng, Yuzhen Xiao, Xinyu Ma, Runchuan Zhu, Xu Chu, Junfeng Zhao and Yasha Wang. 2025. Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.acl-long.571

@inproceedings{xu2025parenting,
  title = {{Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning}},
  author = {Yongxin Xu, Ruizhe Zhang, Xinke Jiang, Yujie Feng, Yuzhen Xiao, Xinyu Ma, Runchuan Zhu, Xu Chu, Junfeng Zhao and Yasha Wang},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.571},
  url = {https://doi.org/10.18653/v1/2025.acl-long.571}
}

References

85 references, 41 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
  2. Edward I Altman, Małgorzata Iwanicz-Drozdowska, Erkki K Laitinen, and Arto Suvas. 2017. Financial distress prediction in an international context: A review and empirical analysis of altman’s z-score model. Journal of international financial management & accounting, 28(2):131–171. find
  3. Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. Self-rag: Learning to retrieve, generate, and critique through self-reflection. In The Twelfth International Conference on Learning Representations.
  4. Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. 2023. Qwen technical report. arXiv preprint arXiv:2309.16609.
  5. Tom B Brown. 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165.
  6. Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712.
  7. Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. 2024. Benchmarking large language models in retrieval-augmented generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 17754–17762.
  8. Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2023. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research, 24(240):1–113.
  9. Yung-Sung Chuang, Yujia Xie, Hongyin Luo, Yoon Kim, James Glass, and Pengcheng He. 2023. Dola: Decoding by contrasting layers improves factuality in large language models. arXiv preprint arXiv:2309.03883.
  10. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018. Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv preprint arXiv:1803.05457.
  11. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168.
  12. Antonia Creswell, Murray Shanahan, and Irina Higgins. Selection-inference: Exploiting large language models for interpretable logical reasoning. In The Eleventh International Conference on Learning Representations. find
  13. Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. 2022. Knowledge neurons in pretrained transformers. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8493– 8502. find
  14. Siqi Fan, Xin Jiang, Xiang Li, Xuying Meng, Peng Han, Shuo Shang, Aixin Sun, Yequan Wang, and Zhongyuan Wang. 2024a. Not all layers of llms are necessary during inference. arXiv preprint arXiv:2403.02181. — [arXiv:2403.02181](https://arxiv.org/abs/2403.02181) find
  15. Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, and Qing Li. 2024b. A survey on rag meeting llms: Towards retrieval-augmented large language models. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 6491– 6501.
  16. Feiteng Fang, Yuelin Bai, Shiwen Ni, Min Yang, Xiaojun Chen, and Ruifeng Xu. 2024. Enhancing noise robustness of retrieval-augmented language models with adaptive adversarial training. arXiv preprint arXiv:2405.20978.
  17. Yujie Feng, Xu Chu, Yongxin Xu, Zexin Lu, Bo Liu, Philip S Yu, and Xiao-Ming Wu. 2024a. Kif: Knowledge identification and fusion for language model continual learning. arXiv preprint arXiv:2408.05200. — [arXiv:2408.05200](https://arxiv.org/abs/2408.05200) find
  18. Yujie Feng, Xu Chu, Yongxin Xu, Guangyuan Shi, Bo Liu, and Xiao-Ming Wu. 2024b. Tasl: Continual dialog state tracking via task skill localization and consolidation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1266–1279. find
  19. Yujie Feng, Zexin Lu, Bo Liu, Liming Zhan, and Xiao-Ming Wu. 2023. Towards llm-driven dialogue state tracking. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 739–755. find
  20. Yujie Feng, Xujia Wang, Zexin Lu, Shenghong Fu, Guangyuan Shi, Yongxin Xu, Yasha Wang, Philip S Yu, Xu Chu, and Xiao-Ming Wu. 2025. Recurrent knowledge identification and fusion for language model continual learning. arXiv preprint arXiv:2502.17510.
  21. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997.
  22. Wes Gurnee, Theo Horsley, Zifan Carl Guo, Tara Rezaei Kheirkhah, Qinyi Sun, Will Hathaway, Neel Nanda, and Dimitris Bertsimas. 2024. Universal neurons in gpt2 language models. arXiv preprint arXiv:2401.12181. — [arXiv:2401.12181](https://arxiv.org/abs/2401.12181) find
  23. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020. Retrieval augmented language model pre-training. In International conference on machine learning, pages 3929–3938. PMLR.
  24. Michael J Hawrylycz, Ed S Lein, Angela L Guillozet-Bongaarts, Elaine H Shen, Lydia Ng, Jeremy A Miller, Louie N Van De Lagemaat, Kimberly A Smith, Amanda Ebbert, Zackery L Riley, et al. 2012. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature, 489(7416):391– 399. find
  25. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. In International Conference on Learning Representations. Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu C…
  26. Xinke Jiang, Yue Fang, Rihong Qiu, Haoyu Zhang, Yongxin Xu, Hao Chen, Wentao Zhang, Ruizhe Zhang, Yuchen Fang, Xu Chu, et al. 2024a. Tc-rag: Turing-complete rag’s case study on medical llm systems. arXiv preprint arXiv:2408.09199.
  27. Xinke Jiang, Ruizhe Zhang, Yongxin Xu, Rihong Qiu, Yue Fang, Zhiyuan Wang, Jinyi Tang, Hongxin Ding, Xu Chu, Junfeng Zhao, et al. 2024b. Hykge: A hypothesis knowledge graph enhanced framework for accurate and reliable medical llms responses. arXiv preprint arXiv:2312.15883. — [arXiv:2312.15883](https://arxiv.org/abs/2312.15883) find
  28. Zhengbao Jiang, Frank F Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023. Active retrieval augmented generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7969–7992.
  29. Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Li Qiuxia, and Jun Zhao. 2024a. find
  30. Tug-of-war between knowledge: Exploring and resolving knowledge conflicts in retrieval-augmented language models. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 16867–16878.
  31. Xinyu Ma, Yasha Wang, Xu Chu, Liantao Ma, Wen Tang, Junfeng Zhao, Ye Yuan, and Guoren Wang. 2023. Patient health representation learning via correlational sparse prior of medical features. IEEE Transactions on Knowledge and Data Engineering, 35(11):11769–11783. find
  32. Zhuoran Jin, Pengfei Cao, Hongbang Yuan, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, and Jun Zhao. 2024b. Cutting off the head ends the conflict: A mechanism for interpreting and mitigating knowledge conflicts in language models. In Findings of the Association for Computational Lingu… find
  33. Xinyu Ma, Yifeng Xu, Yang Lin, Tianlong Wang, Xu Chu, Xin Gao, Junfeng Zhao, and Yasha Wang. 2025. DRESSing up LLM: Efficient stylized question-answering via style subspace editing. In The Thirteenth International Conference on Learning Representations. find
  34. Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2019. Generalization through memorization: Nearest neighbor language models. arXiv preprint arXiv:1911.00172. find
  35. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33:9459–9474.
  36. Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, and Sanjiv Kumar. 2023a. Large language models with controllable working memory. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1774–1793.
  37. Jianquan Li, Xidong Wang, Xiangbo Wu, Zhiyi Zhang, Xiaolong Xu, Jie Fu, Prayag Tiwari, Xiang Wan, and Benyou Wang. 2023b. Huatuo-26m, a large-scale chinese medical qa dataset. arXiv preprint arXiv:2305.01526. find
  38. Stephanie Lin, Jacob Hilton, and Owain Evans. 2022. Truthfulqa: Measuring how models mimic human falsehoods. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3214–3252.
  39. Yang Lin, Xinyu Ma, Xu Chu, Yujie Jin, Zhibang Yang, Yasha Wang, and Hong Mei. 2024. Lora dropout as a sparsity regularizer for overfitting control. arXiv preprint arXiv:2404.09610. — [arXiv:2404.09610](https://arxiv.org/abs/2404.09610) find
  40. Yantao Liu, Zijun Yao, Xin Lv, Yuchen Fan, Shulin Cao, Jifan Yu, Lei Hou, and Juanzi Li. 2024. Untangle the knot: Interweaving conflicting knowledge and reasoning skills in large language models. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resourc… find
  41. Xinyu Ma, Xu Chu, Zhibang Yang, Yang Lin, Xin Gao, and Junfeng Zhao. 2024. Parameter efficient quasi-orthogonal fine-tuning via givens rotation. In Proceedings of the 41st International Conference on Machine Learning, volume 235, pages 33686–33729. PMLR. find
  42. Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. When not to trust language models: Investigating effectiveness of parametric and non-parametric memories. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (V… find
  43. Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems, 35:17359–17372. find
  44. Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, and Jan Kautz. 2019. Importance estimation for neural network pruning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11264–11272. find
  45. Vinod Nair and Geoffrey E Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), pages 807–814. find
  46. David S Olton, James T Becker, and Gail E Handelmann. 1979. Hippocampus, space, and memory. Behavioral and Brain sciences, 2(3):313–322. find
  47. Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, et al. 2023. Check your facts and try again: Improving large language models with external knowledge and automated feedback. arXiv preprint arXiv:2302.12813.
  48. M Victoria Puig and Allan T Gulledge. 2011. Serotonin and prefrontal cortex function: neurons, networks, and circuits. Molecular neurobiology, 44:449–464. find
  49. Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018. Know what you don’t know: Unanswerable questions for squad. Preprint, arXiv:1806.03822.
  50. Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hao Tian, Hua Wu, Ji-Rong Wen, and Haifeng Wang. 2023. Investigating the factual knowledge boundary of large language models with retrieval augmentation. arXiv preprint arXiv:2307.11019.
  51. Giacomo Rizzolatti and Laila Craighero. 2004. The mirror-neuron system. Annu. Rev. Neurosci., 27(1):169–192. find
  52. Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2021. Winogrande: An adversarial winograd schema challenge at scale. Communications of the ACM, 64(9):99–106. find
  53. Dan Shi, Renren Jin, Tianhao Shen, Weilong Dong, Xinwei Wu, and Deyi Xiong. 2024a. Ircan: Mitigating knowledge conflicts in llm generation via identifying and reweighting context-aware neurons. arXiv preprint arXiv:2406.18406. — [arXiv:2406.18406](https://arxiv.org/abs/2406.18406) find
  54. Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou. 2023. Large language models can be easily distracted by irrelevant context. In International Conference on Machine Learning, pages 31210–31227. PMLR.
  55. Weijia Shi, Xiaochuang Han, Mike Lewis, Yulia Tsvetkov, Luke Zettlemoyer, and Wen-tau Yih. 2024b. Trusting your evidence: Hallucinate less with context-aware decoding. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language…
  56. Weihang Su, Yichen Tang, Qingyao Ai, Zhijing Wu, and Yiqun Liu. 2024. Dragin: Dynamic retrieval augmented generation based on the real-time information needs of large language models. arXiv preprint arXiv:2403.10081.
  57. Hexiang Tan, Fei Sun, Wanli Yang, Yuanzhuo Wang, Qi Cao, and Xueqi Cheng. 2024. Blinded by generated contexts: How language models merge generated and retrieved contexts for open-domain qa? arXiv preprint arXiv:2401.11911. — [arXiv:2401.11911](https://arxiv.org/abs/2401.11911) find
  58. Qingyu Tan, Hwee Tou Ng, and Lidong Bing. 2023. Towards benchmarking and improving the temporal reasoning capability of large language models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14820–14835. find
  59. Tianyi Tang, Wenyang Luo, Haoyang Huang, Dongdong Zhang, Xiaolei Wang, Xin Zhao, Furu Wei, and Ji-Rong Wen. 2024. Language-specific neurons: The key to multilingual capabilities in large language models. arXiv preprint arXiv:2402.16438.
  60. Qwen Team. 2024. Introducing qwen1. 5. find
  61. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288.
  62. Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022. MuSiQue: Multi-hop questions via single-hop question composition. Transactions of the Association for Computational Linguistics. find
  63. Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, et al. 2024a. Knowledge mechanisms in large language models: A survey and perspective. arXiv preprint arXiv:2407.15017. find
  64. Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, et al. 2023a. Knowledge editing for large language models: A survey. arXiv preprint arXiv:2310.16218.
  65. Xiao Wang, Tianze Chen, Qiming Ge, Han Xia, Rong Bao, Rui Zheng, Qi Zhang, Tao Gui, and Xuan-Jing Huang. 2023b. Orthogonal subspace learning for language model continual learning. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10658–10671. find
  66. Xidong Wang, Guiming Chen, Song Dingjie, Zhang Zhiyi, Zhihong Chen, Qingying Xiao, Junying Chen, Feng Jiang, Jianquan Li, Xiang Wan, et al. 2024b. Cmb: A comprehensive medical benchmark in chinese. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computation…
  67. T Wolf. 2019. Huggingface’s transformers: State-ofthe-art natural language processing. arXiv preprint arXiv:1910.03771.
  68. Kevin Wu, Eric Wu, and James Zou. 2024a. Clasheval: Quantifying the tug-of-war between an llm’s internal prior and external evidence. Preprint.
  69. Kevin Wu, Eric Wu, and James Zou. 2024b. How faithful are rag models? quantifying the tug-of-war between rag and llms’ internal prior. arXiv preprint arXiv:2404.10198.
  70. Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff, Defu Lian, and Jian-Yun Nie. 2023. C-pack: Packaged resources to advance general chinese embedding. arXiv preprint arXiv:2309.07597.
  71. Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su. Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts. In The Twelfth International Conference on Learning Representations.
  72. Shicheng Xu, Liang Pang, Mo Yu, Fandong Meng, Huawei Shen, Xueqi Cheng, and Jie Zhou. 2024a. Unsupervised information refinement training of large language models for retrieval-augmented generation. arXiv preprint arXiv:2402.18150. find
  73. Yongxin Xu, Xu Chu, Kai Yang, Zhiyuan Wang, Peinie Zou, Hongxin Ding, Junfeng Zhao, Yasha Wang, and Bing Xie. 2023a. Seqcare: Sequential training with external medical knowledge graph for diagnosis prediction in healthcare data. In Proceedings of the ACM Web Conference 2023, pages 2819–2830. find
  74. Yongxin Xu, Xinke Jiang, Xu Chu, Rihong Qiu, Yujie Feng, Hongxin Ding, Junfeng Zhao, Yasha Wang, and Bing Xie. 2025. Dearllm: Enhancing personalized healthcare via large language models-deduced feature correlations. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 9… find
  75. Yongxin Xu, Xinke Jiang, Xu Chu, Yuzhen Xiao, Chaohe Zhang, Hongxin Ding, Junfeng Zhao, Yasha Wang, and Bing Xie. 2024b. Protomix: Augmenting health status representation learning via prototype-based mixup. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, page… find
  76. Yongxin Xu, Kai Yang, Chaohe Zhang, Peinie Zou, Zhiyuan Wang, Hongxin Ding, Junfeng Zhao, Yasha Wang, and Bing Xie. 2023b. Vecocare: Visit sequences-clinical notes joint learning for diagnosis prediction in healthcare data. In IJCAI, volume 23, pages 4921–4929. find
  77. Kai Yang, Yongxin Xu, Peinie Zou, Hongxin Ding, Junfeng Zhao, Yasha Wang, and Bing Xie. 2023. Ker-print: local-global knowledge graph enhanced diagnosis prediction for retrospective and prospective interpretations. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 53… find
  78. Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant. Making retrieval-augmented language models robust to irrelevant context. In The Twelfth International Conference on Learning Representations.
  79. Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019. Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4791–4800. find
  80. Qingru Zhang, Minshuo Chen, Alexander Bukharin, Nikos Karampatziakis, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao. 2023. Adalora: Adaptive budget allocation for parameter-efficient fine-tuning. arXiv preprint arXiv:2303.10512.
  81. Qingru Zhang, Simiao Zuo, Chen Liang, Alexander Bukharin, Pengcheng He, Weizhu Chen, and Tuo Zhao. 2022. Platon: Pruning large transformer models with upper confidence bound of weight importance. In International conference on machine learning, pages 26809–26823. PMLR. find
  82. Ruizhe Zhang, Yongxin Xu, Yuzhen Xiao, Runchuan Zhu, Xinke Jiang, Xu Chu, Junfeng Zhao, and Yasha Wang. 2025. Knowpo: Knowledge-aware preference optimization for controllable knowledge selection in retrieval-augmented language models. In Proceedings of the AAAI Conference on Artificial Intelligence,… find
  83. Ruochen Zhao, Xingxuan Li, Shafiq Joty, Chengwei Qin, and Lidong Bing. 2023. Verify-and-edit: A knowledge-enhanced chain-of-thought framework.
  84. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5823–5840. find
  85. Wenxuan Zhou, Sheng Zhang, Hoifung Poon, and Muhao Chen. 2023. Context-faithful prompting for large language models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14544–14556.

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/xu-2025-parenting-optimizing-knowledge-selection-retrieval/