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   Zhengyuan Yang, Yijuan Lu (January 2021, IEEE Conference on Computer Vision and Pattern Recognition)

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2. Search for $${\text {Z}{}{}} {\text {Z}{}{}} $$ and $${\text {Z}{}{}} {\text {H}{}{}} $$ production in the $${\text {b}{}{}} {\bar{{\text {b}{}{}}}{}{}} {\text {b}{}{}} {\bar{{\text {b}{}{}}}{}{}} $$ final state using proton-proton collisions at $$\sqrt{s}=13\,\text {Te}\hspace{-.08em}\text {V} $$

   https://doi.org/10.1140/epjc/s10052-024-13021-z  

   Hayrapetyan, A; Tumasyan, A; Adam, W; Andrejkovic, J W; Bergauer, T; Chatterjee, S; Damanakis, K; Dragicevic, M; Hussain, P S; Jeitler, M; et al (July 2024, The European Physical Journal C)

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   https://doi.org/10.1140/epjc/s10052-018-5950-6  

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4. MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers

   Krishna Pillutla, Swabha Swayamdipta (December 2021, Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021))
5. MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers

   Pillutla, Krishna; Swayamdipta, Swabha; Zellers, Rowan; Thickstun, John; Welleck, Sean; hoi, Yejin; Harchaoui, Zaid (January 2022, Advances in neural information processing systems)

   As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem. We introduce MAUVE, a comparison measure for open-ended text generation, which directly compares the learnt distribution from a text generation model to the distribution of human-written text using divergence frontiers. MAUVE scales up to modern text generation models by computing information divergences in a quantized embedding space. Through an extensive empirical study on three open-ended generation tasks, we find that MAUVE identifies known properties of generated text, scales naturally with model size, and correlates with human judgments, with fewer restrictions than existing distributional evaluation metrics. 

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