text stringlengths 1.6k 4.35k | source stringclasses 1
value |
|---|---|
2024-04-10 Best Practices and Lessons Learned on Synthetic Data for Language Models Ruibo Liu1, Jerry Wei1, Fangyu Liu1, Chenglei Si2, Yanzhe Zhang3, Jinmeng Rao1, Steven Zheng1, Daiyi Peng1, Diyi Yang2, Denny Zhou1and Andrew M. Dai1 1Google Deep Mind,2Stanford University,3Georgia Institute of Technology The success of... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models The rapid advancement of artificial intelligence (AI) technologies has led to their widespread adoption across numerous domains, from assistant agents (e. g., ACT-1, from Adept AI1) and software development (e. g., Devin, from Cognition Lab2) to h... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models 2. Synthetic Data in Training Synthetic data, which is generated by mimicking authentic data collected from the real world, has proven to be an effective and relatively low-cost alternative of real data. This section explores several notable domai... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models prompts to enhance the complexity and diversity of synthetic data. Meanwhile, Magicoder (Wei et al., 2023c) developed OSS-INSTRUCT, which generates 75K diverse synthetic instruction samples from open-source code snippets. Other reasoning tasks. Sy... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models 2. 3. Multimodality Reverse rendering from vision to text. Vision-language alignment data focuses on accurately grounding visual input to an LLM (usually via a vision encoder). Web-scraped image-caption pairs have been the most popular MM alignmen... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models performance in multilingual and cross-lingual question answering (Abulkhanov et al., 2023; Asai et al., 2021; Chi et al., 2020; Kumar et al., 2019; Li and Callison-Burch, 2023; Riabi et al., 2021). One approach is to translate existing monolingual... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models 2023c). This method resulted in a significant improvement in performance on the Truthful QA (Lin et al., 2022) dataset (Zhang et al., 2023c). Similarly, Jones et al. (2023) designed a synthetic task where hallucinations can be readily evaluated, u... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models 3. Synthetic Data in Evaluation Synthetic data is widely used in evaluations of different perspectives: Factuality. AI systems may generate information or responses that are not grounded in factual knowledge or data, leading to the creation of mis... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models 4. Challenges and Limitations of Synthetic Data While synthetic data offers numerous benefits and applications, it is crucial to acknowledge and address the potential challenges and limitations associated with its use. This section delves into thr... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models the model is trained with synthetic data. Synthetic data might include rephrased versions of the benchmark data (Mattern et al., 2023; Oren et al., 2023), rendering token-level decontamination ineffective. In addition to developing more advanced e... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models a narrowed down (Cheng et al., 2023) or over-simplified (Zhou et al., 2024) scenes. Looking forward, another growing direction could be how to achieve scalable oversight more efficiently—given that we have the full control over the synthetic data ... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models cross-lingual alignment. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2098-2102, 2023. M. Ahn,A. Brohan,N. Brown,Y. Chebotar,O. Cortes,B. David,C. Finn,K. Gopalakrishnan,... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models L. Maggiore, C. Jones, A. Cassirer, A. Brock, M. Paganini, G. Irving, O. Vinyals, S. Osindero, K. Simonyan, J. W. Rae, E. Elsen, and L. Sifre. Improving language models by retrieving from trillions of tokens. In K. Chaudhuri, S. Jegelka, L. Song, ... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models Association for Computational Linguistics. doi: 10. 18653/v1/2023. emnlp-main. 669. URL https: //aclanthology. org/2023. emnlp-main. 669. E. Chern, H. Zou, X. Li, J. Hu, K. Feng, J. Li, and P. Liu. Generative ai for math: Abel. https: //github. co... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models T. Falke, L. F. R. Ribeiro, P. A. Utama, I. Dagan, and I. Gurevych. Ranking generated summaries by correctness: An interesting but challenging application for natural language inference. In Proceedings of the 57th Annual Meeting of the Association... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models M. Graça, Y. Kim, J. Schamper, S. Khadivi, and H. Ney. Generalizing back-translation in neural machine translation. In Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers), pages 45-52, Florence, Italy, 2019. Ass... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models J. Huang, S. Gu, L. Hou, Y. Wu, X. Wang, H. Yu, and J. Han. Large language models can self-improve. In H. Bouamor, J. Pino, and K. Bali, editors, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1051-10... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models H. Le, Y. Wang, A. D. Gotmare, S. Savarese, and S. C. H. Hoi. Coderl: Mastering code generation through pretrained models and deep reinforcement learning. Advances in Neural Information Processing Systems, 35:21314-21328, 2022. Y. Le Cun. A path t... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models S. Lin, J. Hilton, and O. Evans. Truthful QA: 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, Dublin, Ireland, 2022. ... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models Z. Luo, C. Xu, P. Zhao, Q. Sun, X. Geng, W. Hu, C. Tao, J. Ma, Q. Lin, and D. Jiang. Wizardcoder: Empowering code large language models with evol-instruct. Ar Xiv preprint, abs/2306. 08568, 2023b. URL https://arxiv. org/abs/2306. 08568. B. Marie, ... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models A. Pan, K. Bhatia, and J. Steinhardt. The effects of reward misspecification: Mapping and mitigat-ing misaligned models. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. Open Review. n... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models O. Vinyals, K. Ayoub, J. Stanway, L. Bennett, D. Hassabis, K. Kavukcuoglu, and G. Irving. Scaling language models: Methods, analysis & insights from training gopher, 2021. URL https://arxiv. org/abs/2112. 11446. R. Rafailov, A. Sharma, E. Mitchell... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models M. Sharma, M. Tong, T. Korbak, D. Duvenaud, A. Askell, S. R. Bowman, E. DURMUS,Z. Hatfield-Dodds, S. R. Johnston, S. M. Kravec, T. Maxwell, S. Mc Candlish, K. Ndousse, O. Rausch, N. Schiefer, D. Yan, M. Zhang, and E. Perez. Towards understanding s... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models E. Todorov, T. Erez, and Y. Tassa. Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 5026-5033. IEEE, 2012. doi: 10. 1109/IROS. 2012. 6386109. H. Touvron, L. Martin... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models J. Wei, D. Huang, Y. Lu, D. Zhou, and Q. V. Le. Simple synthetic data reduces sycophancy in large language models, 2023b. URL https://arxiv. org/abs/2308. 03958. J. Wei, C. Yang, X. Song, Y. Lu, N. Hu, D. Tran, D. Peng, R. Liu, D. Huang, C. Du, an... | SynData.pdf |
Best Practices and Lessons Learned on Synthetic Data for Language Models E. Zelikman, Y. Wu, and N. D. Goodman. Star: Bootstrapping reasoning with reasoning. In Neur IPS, 2022. URL https://api. semanticscholar. org/Corpus ID:247762790. J. Zhang, X. Xu, and S. Deng. Exploring collaboration mechanisms for llm agents: A s... | SynData.pdf |
README.md exists but content is empty.
- Downloads last month
- 5