{"type":"ScholarlyArticle","title":"LEKA: LLM-Enhanced Knowledge Augmentation","authors":[{"name":"Xinhao Zhang","slug":"xinhao-zhang","given":"Xinhao","family":"Zhang","affiliation":"Portland State University , USA"},{"name":"Jinghan Zhang","slug":"jinghan-zhang","given":"Jinghan","family":"Zhang","affiliation":"Portland State University , USA"},{"name":"Fengran Mo","slug":"fengran-mo","given":"Fengran","family":"Mo","affiliation":"University of Montreal , Canada"},{"name":"Dongjie Wang","slug":"dongjie-wang","given":"Dongjie","family":"Wang","affiliation":"University of Kansas , USA"},{"name":"Yanjie Fu","slug":"yanjie-fu","given":"Yanjie","family":"Fu","affiliation":"Arizona State University , USA"},{"name":"Kunpeng Liu","slug":"kunpeng-liu","given":"Kunpeng","family":"Liu","affiliation":"Portland State University , USA"}],"abstract":"Humans excel in analogical learning and knowledge transfer and, more importantly, possess a unique understanding of identifying appropriate sources of knowledge. From a model's perspective, this presents a unique challenge. If models could autonomously retrieve knowledge relevant for transfer or decision-making to solve problems, they would transition from passively acquiring to actively accessing and learning from knowledge. However, filling models with knowledge is relatively straightforward—it simply requires more training and accessible knowledge bases. The more complex task is teaching models about which knowledge can be analogized and transferred. Therefore, we design a knowledge augmentation method, LEKA, for knowledge transfer that actively searches for suitable knowledge sources that can enrich the target domain's knowledge. This LEKA method extracts key information from the target domain's textual information, retrieves pertinent data from external data libraries, and harmonizes retrieved data with the target domain data in feature space and marginal probability measures. We validate the effectiveness of our approach through extensive experiments across various domains and demonstrate significant improvements over traditional methods in automating data alignment and optimizing transfer learning outcomes.","doi":"10.24963/ijcai.2025/781","url":"https://ijcai.org/proceedings/2025/781","pdf":"https://ijcai.org/proceedings/2025/0781.pdf","markdown":"https://ijcai.org/proceedings/2025/781.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/781","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":781,"pages":{"first":7020,"last":7028},"keywords":["Machine Learning: ML: Feature extraction, selection and dimensionality reduction"],"videos":[],"references":[{"position":1,"raw":"[Alyafeai et al., 2020] Zaid Alyafeai, Maged Saeed Al- Shaibani, and Irfan Ahmad. A survey on transfer learning in natural language processing. arXiv preprint arXiv:2007.04239, 2020.","title":"A survey on transfer learning in natural language processing","authors":"Zaid Alyafeai, Maged Saeed Al-Shaibani, Irfan Ahmad","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Arik and Pfister, 2021] Sercan Ö Arik and Tomas Pfister. Tabnet: Attentive interpretable tabular learning. In Pro- ceedings of the AAAI conference on artificial intelligence, volume 35(8), pages 6679-6687, 2021.","title":"Tabnet: Attentive interpretable tabular learning","authors":"Pfister ; Arik, Ö Sercan, Tomas Arik, Pfister","year":"2021","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bhatt, 2019] Himanshu Bhatt. Vehicle Insurance Data. https://www.kaggle.com/datasets/junglisher/ vehicle-insurance-data, 2019. Accessed: 2025-01-21.","title":"Vehicle Insurance Data","authors":"Himanshu Bhatt","year":"2019","venue":null,"doi":null,"url":"https://www.kaggle.com/datasets/junglisher/vehicle-insurance-data","internal_url":null},{"position":4,"raw":"[BlastChar, 2018] BlastChar. Telco Customer Churn. https://www.kaggle.com/datasets/blastchar/ telco-customer-churn, 2018. Accessed: 2025-01-21.","title":"BlastChar. Telco Customer Churn","authors":"Blastchar","year":"2018","venue":null,"doi":null,"url":"https://www.kaggle.com/datasets/blastchar/telco-customer-churn,2018","internal_url":null},{"position":5,"raw":"[Borisov et al., 2022] Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci. Lan- guage models are realistic tabular data generators. arXiv preprint arXiv:2210.06280, 2022.","title":"Language models are realistic tabular data generators","authors":"Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cao et al., 2009] Longbing Cao, Vladimir Gorodetsky, and Pericles A Mitkas. Agent mining: The synergy of agents and data mining. IEEE intelligent systems, 24(3):64-72, 2009.","title":"Longbing Cao, Vladimir Gorodetsky, and Pericles A Mitkas. Agent mining: The synergy of agents and data mining","authors":"Cao","year":"2009","venue":"IEEE intelligent systems","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen and Guestrin, 2016] Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowl- edge discovery and data mining, pages 785-794, 2016.","title":"Xgboost: A scalable tree boosting system","authors":"Tianqi Chen, Carlos Guestrin","year":"2016","venue":"Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Durante et al., 2024] Zane Durante, Qiuyuan Huang, Naoki Wake, Ran Gong, Jae Sung Park, Bidipta Sarkar, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Yejin Choi, et al. Agent ai: Surveying the horizons of multimodal in- teraction. arXiv preprint arXiv:2401.03568, 2024.","title":"Agent ai: Surveying the horizons of multimodal interaction","authors":"Zane Durante, Qiuyuan Huang, Naoki Wake, Ran Gong, Jae Sung Park, Bidipta Sarkar, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Yejin Choi","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Edge et al., 2024] Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Tru- itt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson. From local to global: A graph rag ap- proach to query-focused summarization. arXiv preprint arXiv:2404.16130, 2024.","title":"From local to global: A graph rag approach to query-focused summarization","authors":"Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, Jonathan Larson","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Exa, 2024] Exa. Exa API. https://exa.ai/, 2024. Accessed: 2025-01-10.","title":"Exa. Exa API","authors":null,"year":"2025","venue":null,"doi":null,"url":"https://exa.ai/,2024","internal_url":null},{"position":11,"raw":"[Feng et al., 2021] Steven Y Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. A survey of data augmentation approaches for nlp. arXiv preprint arXiv:2105.03075, 2021.","title":"A survey of data augmentation approaches for nlp","authors":"Varun Steven Y Feng, Jason Gangal, Sarath Wei, Soroush Chandar, Teruko Vosoughi, Eduard Mitamura, Hovy","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Fleischer et al., 2024] Daniel Fleischer, Moshe Berchansky, Moshe Wasserblat, and Peter Izsak. Rag foundry: A framework for enhancing llms for retrieval augmented generation. arXiv preprint arXiv:2408.02545, 2024.","title":"Rag foundry: A framework for enhancing llms for retrieval augmented generation","authors":"Fleischer","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Gao et al., 2023] Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997, 2023.","title":"Retrieval-augmented generation for large language models: A survey","authors":"Gao","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Guo et al., 2017] Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. Deepfm: a factorization-machine based neural network for ctr predic- tion. arXiv preprint arXiv:1703.04247, 2017.","title":"Deepfm: a factorization-machine based neural network for ctr prediction","authors":"Guo","year":"2017","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2017/239"},{"position":15,"raw":"[Han et al., 2021] Wenjuan Han, Bo Pang, and Yingnian Wu. Robust transfer learning with pretrained language models through adapters. arXiv preprint arXiv:2108.02340, 2021.","title":"Robust transfer learning with pretrained language models through adapters","authors":"Wenjuan Han, Bo Pang, Yingnian Wu","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Hu and Lu, 2024] Yucheng Hu and Yuxing Lu. Rag and rau: A survey on retrieval-augmented language model in natu- ral language processing. arXiv preprint arXiv:2404.19543, 2024.","title":"Rag and rau: A survey on retrieval-augmented language model in natural language processing","authors":"Lu ; ] Yucheng Hu, Yuxing Hu, Lu","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Huang and Huang, 2024] Yizheng Huang and Jimmy Huang. A survey on retrieval-augmented text gen- eration for large language models. arXiv preprint arXiv:2404.10981, 2024.","title":"A survey on retrieval-augmented text generation for large language models","authors":"Huang Huang, Yizheng Huang, Jimmy Huang","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Huang et al., 2020] Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar Karnin. Tabtransformer: Tabu- lar data modeling using contextual embeddings. arXiv preprint arXiv:2012.06678, 2020.","title":"Tabtransformer: Tabular data modeling using contextual embeddings","authors":"Xin Huang, Ashish Khetan, Milan Cvitkovic, Zohar Karnin","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Huang et al., 2024] Yiming Huang, Xiao Liu, Yeyun Gong, Zhibin Gou, Yelong Shen, Nan Duan, and Weizhu Chen. Key-point-driven data synthesis with its en- hancement on mathematical reasoning. arXiv preprint arXiv:2403.02333, 2024.","title":"Key-point-driven data synthesis with its enhancement on mathematical reasoning","authors":"Yiming Huang, Xiao Liu, Yeyun Gong, Zhibin Gou, Yelong Shen, Nan Duan, Weizhu Chen","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Janosi et al., 1989] Andras Janosi, William Steinbrunn, Matthias Pfisterer, and Robert Detrano. Heart Disease. https://archive.ics.uci.edu/dataset/45/heart+disease, 1989. Accessed: 2025-01-21.","title":"Heart Disease","authors":"Andras Janosi, William Steinbrunn, Matthias Pfisterer, Robert Detrano","year":"2025","venue":null,"doi":null,"url":"https://archive.ics.uci.edu/dataset/45/heart+disease,1989","internal_url":null},{"position":21,"raw":"[Jin et al., 2024] Chao Jin, Zili Zhang, Xuanlin Jiang, Fangyue Liu, Xin Liu, Xuanzhe Liu, and Xin Jin. Ragcache: Efficient knowledge caching for retrieval- augmented generation. arXiv preprint arXiv:2404.12457, 2024.","title":"Ragcache: Efficient knowledge caching for retrievalaugmented generation","authors":"Jin Chao, Zili Zhang, Xuanlin Jiang, Fangyue Liu, Xin Liu, Xuanzhe Liu, Xin Jin","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Kaggle, 2024] Kaggle. Kaggle: Your Machine Learning and Data Science Community. https://www.kaggle.com/, 2024. Accessed: 2025-01-10.","title":"Kaggle. Kaggle: Your Machine Learning and Data Science Community","authors":"Kaggle","year":"2024","venue":null,"doi":null,"url":"https://www.kaggle.com/,2024","internal_url":null},{"position":23,"raw":"[Khodaee et al., 2024] Pouya Khodaee, Herna L Viktor, and Wojtek Michalowski. Knowledge transfer in lifelong ma- chine learning: a systematic literature review. Artificial Intelligence Review, 57(8):217, 2024.","title":"Knowledge transfer in lifelong machine learning: a systematic literature review","authors":"Pouya Khodaee, L Herna, Wojtek Viktor, Michalowski","year":"2024","venue":"Artificial Intelligence Review","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Levin et al., 2022] Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, and Micah Goldblum. Transfer learning with deep tabular models. arXiv preprint arXiv:2206.15306, 2022.","title":"Transfer learning with deep tabular models","authors":"Levin","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Lewis et al., 2020] Patrick Lewis, Ethan Perez, Aleksan- dra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Infor- mation Processing Systems, 33:9459-9474, 2020.","title":"Retrieval-augmented generation for knowledge-intensive nlp tasks","authors":"Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-Tau Yih, Tim Rocktäschel","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Li and Ramakrishnan, 2025] Sha Li and Naren Ramakrish- nan. Oreo: A plug-in context reconstructor to en- hance retrieval-augmented generation. arXiv preprint arXiv:2502.13019, 2025.","title":"Oreo: A plug-in context reconstructor to enhance retrieval-augmented generation","authors":"Ramakrishnan Li","year":"2025","venue":"Sha Li and Naren Ramakrishnan","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Lin et al., 2023] Huawei Lin, Jun Woo Chung, Yingjie Lao, and Weijie Zhao. Machine unlearning in gradient boosting decision trees. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1374-1383, 2023.","title":"Machine unlearning in gradient boosting decision trees","authors":"Lin","year":"2023","venue":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Liu et al., 2021] Kunpeng Liu, Yanjie Fu, Le Wu, Xi- aolin Li, Charu Aggarwal, and Hui Xiong. Automated feature selection: A reinforcement learning perspective. IEEE Transactions on Knowledge and Data Engineering, 35(3):2272-2284, 2021.","title":"Automated feature selection: A reinforcement learning perspective","authors":"Kunpeng Liu, Yanjie Fu, Le Wu, Xiaolin Li, Charu Aggarwal, Hui Xiong","year":"2021","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Nam et al., 2024] Jaehyun Nam, Woomin Song, Seong Hyeon Park, Jihoon Tack, Sukmin Yun, Jae- hyung Kim, Kyu Hwan Oh, and Jinwoo Shin. Tabular transfer learning via prompting llms. arXiv preprint arXiv:2408.11063, 2024.","title":"Tabular transfer learning via prompting llms","authors":"Nam","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[OpenAI, 2024] OpenAI. GPT-4o. https://platform.openai. com/docs/models/gpt-4o, 2024. Accessed: 2025-01-10.","title":"OpenAI. GPT-4o","authors":"Openai","year":"2024","venue":null,"doi":null,"url":"https://platform.openai.com/docs/models/gpt-4o,2024","internal_url":null},{"position":31,"raw":"[Rigatti, 2017] Steven J Rigatti. Random forest. Journal of Insurance Medicine, 47(1):31-39, 2017.","title":"Random forest","authors":"J Steven, Rigatti","year":"2017","venue":"Journal of Insurance Medicine","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Ringwald and Stiefelhagen, 2021] Tobias Ringwald and Rainer Stiefelhagen. Adaptiope: A modern benchmark for unsupervised domain adaptation. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 101-110, 2021.","title":"Adaptiope: A modern benchmark for unsupervised domain adaptation","authors":"Stiefelhagen Ringwald, Tobias Ringwald, Rainer Stiefelhagen","year":"2021","venue":"Proceedings of the IEEE/CVF winter conference on applications of computer vision","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Seemakhupt et al., 2024] Korakit Seemakhupt, Sihang Liu, and Samira Khan. Edgerag: Online-indexed rag for edge devices. arXiv preprint arXiv:2412.21023, 2024.","title":"Edgerag: Online-indexed rag for edge devices","authors":"Seemakhupt","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Shi et al., 2018] Hongtao Shi, Hongping Li, Dan Zhang, Chaqiu Cheng, and Xuanxuan Cao. An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification. Computer Networks, 132:81-98, 2018.","title":"An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification","authors":"Hongtao Shi, Hongping Li, Dan Zhang, Chaqiu Cheng, Xuanxuan Cao","year":"2018","venue":"Computer Networks","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Siriwardhana et al., 2023] Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Tharindu Kaluarachchi, Rajib Rana, and Suranga Nanayakkara. Improving the domain adaptation of retrieval augmented generation (rag) mod- els for open domain question answering. Transactions of the Association for Computational Linguistics, 11:1-17, 2023.","title":"Improving the domain adaptation of retrieval augmented generation (rag) models for open domain question answering","authors":"Siriwardhana","year":"2023","venue":"Transactions of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Tang et al., 2020] Zhiqiang Tang, Yunhe Gao, Leonid Kar- linsky, Prasanna Sattigeri, Rogerio Feris, and Dimitris Metaxas. Onlineaugment: Online data augmentation with less domain knowledge. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part VII 16, pages 313-329. Springer, 2020. [University of California, Irvine, 2024] University of Cali- fornia, Irvine. UCI Machine Learning Repository. https: //archive.ics.uci.edu/, 2024. Accessed: 2025-01-10.","title":"Onlineaugment: Online data augmentation with less domain knowledge","authors":"Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky, Prasanna Sattigeri, Rogerio Feris, Dimitris Metaxas","year":"2020","venue":"Computer Vision-ECCV 2020: 16th European Conference","doi":null,"url":"https://archive.ics.uci.edu/,2024","internal_url":null},{"position":37,"raw":"[Wang and Sun, 2022] Zifeng Wang and Jimeng Sun. Transtab: Learning transferable tabular transformers across tables. Advances in Neural Information Processing Systems, 35:2902-2915, 2022.","title":"Transtab: Learning transferable tabular transformers across tables","authors":"Wang, Sun ;, Zifeng Wang, Jimeng Sun","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wang et al., 2022] Limin Wang, Xinhao Zhang, Kuo Li, and Shuai Zhang. Semi-supervised learning for k- dependence bayesian classifiers. Applied Intelligence, pages 1-19, 2022.","title":"Semi-supervised learning for kdependence bayesian classifiers","authors":"Limin Wang, Xinhao Zhang, Kuo Li, Shuai Zhang","year":"2022","venue":"Applied Intelligence","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wang et al., 2025] Zaitian Wang, Jinghan Zhang, Xinhao Zhang, Kunpeng Liu, Pengfei Wang, and Yuanchun Zhou. Diversity-oriented data augmentation with large language models. arXiv preprint arXiv:2502.11671, 2025.","title":"Diversity-oriented data augmentation with large language models","authors":"Wang","year":"2025","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wolberg et al., 1995] William Wolberg, Olvi Mangasarian, Nick Street, and W. Street. Breast Cancer Wisconsin (Di- agnostic). https://archive.ics.uci.edu/dataset/17/breast+ cancer+wisconsin+diagnostic, 1995. Accessed: 2025-01- 21.","title":"Breast Cancer Wisconsin (Diagnostic","authors":"William Wolberg, Olvi Mangasarian, Nick Street, W Street","year":"2025","venue":null,"doi":null,"url":"https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic,1995","internal_url":null},{"position":41,"raw":"[Xie et al., 2024] Henry J Xie, Jinghan Zhang, Xinhao Zhang, and Kunpeng Liu. Scoring with large language models: A study on measuring empathy of responses in dialogues. arXiv preprint arXiv:2412.20264, 2024.","title":"Scoring with large language models: A study on measuring empathy of responses in dialogues","authors":"Jinghan Henry J Xie, Xinhao Zhang, Kunpeng Zhang, Liu","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Yordanov et al., 2021] Yordan Yordanov, Vid Kocijan, Thomas Lukasiewicz, and Oana-Maria Camburu. Few- shot out-of-domain transfer learning of natural language explanations in a label-abundant setup. arXiv preprint arXiv:2112.06204, 2021.","title":"Fewshot out-of-domain transfer learning of natural language explanations in a label-abundant setup","authors":"Yordan Yordanov, Vid Kocijan, Thomas Lukasiewicz, Oana-Maria Camburu","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhang et al., 2024a] Jinghan Zhang, Xiting Wang, Yiqiao Jin, Changyu Chen, Xinhao Zhang, and Kunpeng Liu. Prototypical reward network for data-efficient rlhf. arXiv preprint arXiv:2406.06606, 2024.","title":"Prototypical reward network for data-efficient rlhf","authors":"Zhang","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2024b] Tianjun Zhang, Shishir G Patil, Naman Jain, Sheng Shen, Matei Zaharia, Ion Stoica, and Joseph E Gonzalez. Raft: Adapting language model to domain spe- cific rag. arXiv preprint arXiv:2403.10131, 2024.","title":"Raft: Adapting language model to domain specific rag","authors":"Zhang","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhang et al., 2024c] Xinhao Zhang, Jinghan Zhang, Fen- gran Mo, Yuzhong Chen, and Kunpeng Liu. Tifg: Text- informed feature generation with large language models. arXiv preprint arXiv:2406.11177, 2024.","title":"Tifg: Textinformed feature generation with large language models","authors":"Zhang","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhang et al., 2024d] Xinhao Zhang, Jinghan Zhang, Banaf- sheh Rekabdar, Yuanchun Zhou, Pengfei Wang, and Kun- peng Liu. Dynamic and adaptive feature generation with llm. arXiv preprint arXiv:2406.03505, 2024.","title":"Dynamic and adaptive feature generation with llm","authors":"Zhang","year":"2024","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2025/782"},{"position":47,"raw":"[Zhang et al., 2025a] Jinghan Zhang, Fengran Mo, Xiting Wang, and Kunpeng Liu. Blind spot navigation in llm rea- soning with thought space explorer, 2025.","title":"Blind spot navigation in llm reasoning with thought space explorer","authors":"Zhang","year":"2025","venue":null,"doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Zhang et al., 2025b] Jinghan Zhang, Xiting Wang, Weijiey- ing Ren, Lu Jiang, Dongjie Wang, and Kunpeng Liu. Ratt: A thought structure for coherent and correct llm reason- ing. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39(25), pages 26733-26741, 2025.","title":"Ratt: A thought structure for coherent and correct llm reasoning","authors":"Zhang","year":"2025","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}