Enhancing Data Quality: Conclusion and Future Work, and References

Authors: (1) Wanru Zhao, University of Cambridge, Shanghai AI Laboratory with Equal contribution; (2) Yaxin Du, Shanghai Jiao Tong University with Equal contribution; (3) Nicholas D. Lane, University of Cambridge and Flower Labs; (4) Siheng Chen, Shanghai AI Laboratory and Shanghai Jiao Tong University; (5) Yanfeng Wang, Shanghai AI Laboratory and Shanghai Jiao Tong University. Table of Links Abstract and Introduction Motivation and Setup: How low-quality data affects the performance of Collaborative Training Proposed Workflow for Data Quality Control Experiments Conclusion and Future Work, and References A. Related Work B. Heterogeneity Settings C. Experimental Details D. Ablation study of Unified Scoring with Anchor Data E. Examples for low-and high- quality Data CONCLUSION AND FUTURE WORK In this paper, we establish a data quality control pipeline for federated fine-tuning of LLMs, avoiding directly sharing any private data. Preliminary experiments show that the selected high-quality data ensures an effective and reliable learning process, leading to improved model performance. To further safeguard privacy, we can seamlessly integrate differential privacy or a secure aggregation component to prevent reconstruction attacks, enhancing the security of our framework. REFERENCES Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862, 2022. Together Computer. Redpajama: an open dataset for training large language models, 2023. URL https://github.com/togethercomputer/RedPajama-Data. Shizhe Diao, Rui Pan, Hanze Dong, Ka Shun Shum, Jipeng Zhang, Wei Xiong, and Tong Zhang. Lmflow: An extensible toolkit for finetuning and inference of large foundation models. arXiv preprint arXiv:2306.12420, 2023. 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In International Conference on Machine Learning, pp. 7252–7261. PMLR, 2019. Wanru Zhao, Royson Lee, Yihong Chen, Xinchi Qiu, Yan Gao, Hongxiang Fan, and Nicholas Donald Lane. Breaking physical and linguistic borders: Multilingual federated prompt tuning for lowresource languages. In The Twelfth International Conference on Learning Representations, 2024. Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, et al. Lima: Less is more for alignment. arXiv preprint arXiv:2305.11206, 2023. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Wanru Zhao, University of Cambridge, Shanghai AI Laboratory with Equal contribution; (2) Yaxin Du, Shanghai Jiao Tong University with Equal contribution; (3) Nicholas D. Lane, University of Cambridge and Flower Labs; (4) Siheng Chen, Shanghai AI Laboratory and Shanghai Jiao Tong University; (5) Yanfeng Wang, Shanghai AI Laboratory and Shanghai Jiao Tong University. Authors: Authors: (1) Wanru Zhao, University of Cambridge, Shanghai AI Laboratory with Equal contribution; (2) Yaxin Du, Shanghai Jiao Tong University with Equal contribution; (3) Nicholas D. Lane, University of Cambridge and Flower Labs; (4) Siheng Chen, Shanghai AI Laboratory and Shanghai Jiao Tong University; (5) Yanfeng Wang, Shanghai AI Laboratory and Shanghai Jiao Tong University. Table of Links Abstract and Introduction Motivation and Setup: How low-quality data affects the performance of Collaborative Training Proposed Workflow for Data Quality Control Experiments Conclusion and Future Work, and References A. Related Work B. Heterogeneity Settings C. Experimental Details D. Ablation study of Unified Scoring with Anchor Data E. Examples for low-and high- quality Data Abstract and Introduction Abstract and Introduction Motivation and Setup: How low-quality data affects the performance of Collaborative Training Motivation and Setup: How low-quality data affects the performance of Collaborative Training Proposed Workflow for Data Quality Control Proposed Workflow for Data Quality Control Experiments Experiments Conclusion and Future Work, and References Conclusion and Future Work, and References A. Related Work A. Related Work B. Heterogeneity Settings B. Heterogeneity Settings C. Experimental Details C. Experimental Details D. Ablation study of Unified Scoring with Anchor Data D. Ablation study of Unified Scoring with Anchor Data E. Examples for low-and high- quality Data E. Examples for low-and high- quality Data CONCLUSION AND FUTURE WORK In this paper, we establish a data quality control pipeline for federated fine-tuning of LLMs, avoiding directly sharing any private data. Preliminary experiments show that the selected high-quality data ensures an effective and reliable learning process, leading to improved model performance. To further safeguard privacy, we can seamlessly integrate differential privacy or a secure aggregation component to prevent reconstruction attacks, enhancing the security of our framework. REFERENCES Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862, 2022. Together Computer. Redpajama: an open dataset for training large language models, 2023. URL https://github.com/togethercomputer/RedPajama-Data. Shizhe Diao, Rui Pan, Hanze Dong, Ka Shun Shum, Jipeng Zhang, Wei Xiong, and Tong Zhang. Lmflow: An extensible toolkit for finetuning and inference of large foundation models. arXiv preprint arXiv:2306.12420, 2023. Tianyu Han, Lisa C Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, Daniel Truhn, and Keno K Bressem. Medalpaca–an open-source collection of medical conversational ai models and training data. arXiv preprint arXiv:2304.08247, 2023. Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. Deberta: Decoding-enhanced bert with disentangled attention. arXiv preprint arXiv:2006.03654, 2020. Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, and Jacob Steinhardt. Aligning ai with shared human values. Proceedings of the International Conference on Learning Representations (ICLR), 2021a. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. Proceedings of the International Conference on Learning Representations (ICLR), 2021b. Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre. Training compute-optimal large language models. CoRR, abs/2203.15556, 2022. doi: 10.48550/ ARXIV.2203.15556. URL https://doi.org/10.48550/arXiv.2203.15556. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. CoRR, abs/2106.09685, 2021. URL https://arxiv.org/abs/2106.09685. Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. What disease does this patient have? a large-scale open domain question answering dataset from medical exams. Applied Sciences, 11(14):6421, 2021. Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W Cohen, and Xinghua Lu. Pubmedqa: A dataset for biomedical research question answering. arXiv preprint arXiv:1909.06146, 2019. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. CoRR, abs/2001.08361, 2020. URL https://arxiv.org/abs/2001.08361. Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Yoshua Bengio and Yann LeCun (eds.), 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015. URL http: //arxiv.org/abs/1412.6980. Pang Wei Koh and Percy Liang. Understanding black-box predictions via influence functions. In International conference on machine learning, pp. 1885–1894. PMLR, 2017. Jakub Konecny, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and ` Dave Bacon. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492, 2016. Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi-Rui Tam, Keith Stevens, Abdullah Barhoum, Nguyen Minh Duc, Oliver Stanley, Richárd Nagyfi, et al. Openassistant conversations–democratizing large language model alignment. arXiv preprint arXiv:2304.07327, 2023. Yongchan Kwon, Eric Wu, Kevin Wu, and James Zou. Datainf: Efficiently estimating data influence in lora-tuned llms and diffusion models. arXiv preprint arXiv:2310.00902, 2023. Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234–1240, 2020. Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris CallisonBurch, and Nicholas Carlini. Deduplicating training data makes language models better. arXiv preprint arXiv:2107.06499, 2021. Qinbin Li, Bingsheng He, and Dawn Song. Model-contrastive federated learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10713–10722, 2021. Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems, 2:429–450, 2020. Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net, 2019. URL https://openreview.net/forum?id=Bkg6RiCqY7. Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. Advances in neural information processing systems, 30, 2017. Yizhen Luo, Jiahuan Zhang, Siqi Fan, Kai Yang, Yushuai Wu, Mu Qiao, and Zaiqing Nie. 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