![]() Scale’s Generative AI Data Engine combines automation and human intelligence to generate training data tailored to specific AI goals and data needs rapidly.Īll the responses in the dataset were annotated on five attributes, namely: The Scale AI team then engaged their human experts to evaluate each response on a scale of 0–4. We collected around 10K high-quality prompts and generated four responses for each using an in-house LLM. Let’s dive deep into how we built this dataset. Developers can now guide LLM responses on additional attributes like complexity and verbosity and enhance the overall controllability of the responses for end-users.īy using this new dataset and the SteerLM technique, NVIDIA trained a Llama 2 70B foundational model that outperforms the Llama 2-70B chat model on MT Bench and TruthfulQA MC2 benchmarks. Coupled with the SteerLM technique, it improves the factuality and coherence of responses. HelpSteer is a collaborative effort between our team and Scale AI. This new resource enables developers to get started with using the SteerLM technique quickly and build state-of-the-art custom models. The NVIDIA NeMo team is now open-sourcing a multi-attribute dataset called Helpfulness SteerLM dataset (HelpSteer). The developer community has shown great interest in using the approach for building custom LLMs. This technique enables users to control large language model (LLM) responses during inference. Rev.NVIDIA recently announced the NVIDIA NeMo SteerLM technique as part of the NVIDIA NeMo framework. A 96(3), 032316 (2017)ĭu, S., Bai, Z., Guo, Y.: Conditions for coherence transformations under incoherent operations. Zhu, H., Ma, Z., Cao, Z., Fei, S.-M., Vedral, V.: Operational one-to-one mapping between coherence and entanglement measures. Vidal, G.: Entanglement of pure states for a single copy. Lo, H.-K., Popescu, S.: Concentrating entanglement by local actions: beyond mean values. In: Mathematical Proceedings of the Cambridge Philosophical Society, vol. 31, pp. Schrödinger, E.: Discussion of probability relations between separated systems. Zhao, M.-J., Ma, T., Fei, S.-M.: Coherence of assistance and regularized coherence of assistance. Liu, C., Guo, Y.-Q., Tong, D.: Enhancing coherence of a state by stochastic strictly incoherent operations. Yadin, B., Ma, J., Girolami, D., Gu, M., Vedral, V.: Quantum processes which do not use coherence. Marvian, I., Spekkens, R.W.: How to quantify coherence: distinguishing speakable and unspeakable notions. arXiv:quant-ph/0612146Ĭhitambar, E., Gour, G.: Critical examination of incoherent operations and a physically consistent resource theory of quantum coherence. A 98(5), 052329 (2018b)įang, K., Wang, X., Lami, L., Regula, B., Adesso, G.: Probabilistic distillation of quantum coherence. Regula, B., Lami, L., Streltsov, A.: Nonasymptotic assisted distillation of quantum coherence. Gour, G., Spekkens, R.W.: Entanglement of assistance is not a bipartite measure nor a tripartite monotone. 121(1), 010401 (2018a)Ĭhitambar, E., Streltsov, A., Rana, S., Bera, M., Adesso, G., Lewenstein, M.: Assisted distillation of quantum coherence. Regula, B., Fang, K., Wang, X., Adesso, G.: One-shot coherence distillation. Rains, E.M.: Rigorous treatment of distillable entanglement. A 53(4), 2046 (1996a)īennett, C.H., Brassard, G., Popescu, S., Schumacher, B., Smolin, J.A., Wootters, W.K.: Purification of noisy entanglement and faithful teleportation via noisy channels. 116(12), 120404 (2016)īennett, C.H., Bernstein, H.J., Popescu, S., Schumacher, B.: Concentrating partial entanglement by local operations. Winter, A., Yang, D.: Operational resource theory of coherence. Streltsov, A., Chitambar, E., Rana, S., Bera, M.N., Winter, A., Lewenstein, M.: Entanglement and coherence in quantum state merging. Yu, X.-D., Zhang, D.-J., Xu, G., Tong, D.: Alternative framework for quantifying coherence. Peng, Y., Jiang, Y., Fan, H.: Maximally coherent states and coherence-preserving operations. Streltsov, A., Adesso, G., Plenio, M.B.: Colloquium: quantum coherence as a resource.
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