Gongzheng Li

Also published as: GongZheng Li


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Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model
Gongzheng Li | Yadong Xi | Jingzhen Ding | Duan Wang | Ziyang Luo | Rongsheng Zhang | Bai Liu | Changjie Fan | Xiaoxi Mao | Zeng Zhao
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track

Recently, large-scale transformer-based models have been proven to be effective over various tasks across many domains. Nevertheless, applying them in industrial production requires tedious and heavy works to reduce inference costs. To fill such a gap, we introduce a scalable inference solution: Easy and Efficient Transformer (EET), including a series of transformer inference optimization at the algorithm and implementation levels. First, we design highly optimized kernels for long inputs and large hidden sizes. Second, we propose a flexible CUDA memory manager to reduce the memory footprint when deploying a large model. Compared with the state-of-the-art transformer inference library (Faster Transformer v4.0), EET can achieve an average of 1.40-4.20x speedup on the transformer decoder layer with an A100 GPU.

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Conditioned Masked Language and Image Modeling for Image-Text Dense Retrieval
Ziyang Luo | Yadong Xi | Rongsheng Zhang | GongZheng Li | Zeng Zhao | Jing Ma
Findings of the Association for Computational Linguistics: EMNLP 2022

Image-text retrieval is a fundamental cross-modal task that takes image/text as a query to retrieve relevant data of another type. The large-scale two-stream pre-trained models like CLIP have achieved tremendous success in this area. They embed the images and texts into instance representations with two separate encoders, aligning them on the instance-level with contrastive learning. Beyond this, the following works adopt the fine-grained token-level interaction (Masked Language and Image Modeling) to boost performance further. However, the vanilla token-level objectives are not designed to aggregate the image-text alignment information into the instance representations, but the token representations, causing a gap between pre-training and application. To address this issue, we carefully design two novel conditioned token-level pre-training objectives, Conditioned Masked Language and Image Modeling (ConMLM and ConMIM), forcing models to aggregate the token-level alignment information into the instance representations. Combing with the instance-level contrastive learning, we propose our cross-modal dense retrieval framework, Conditioned Language-Image Pre-training (ConLIP). Experimental results on two popular cross-modal retrieval benchmarks (MSCOCO and Flickr30k) reveal the effectiveness of our methods.


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KuiLeiXi: a Chinese Open-Ended Text Adventure Game
Yadong Xi | Xiaoxi Mao | Le Li | Lei Lin | Yanjiang Chen | Shuhan Yang | Xuhan Chen | Kailun Tao | Zhi Li | Gongzheng Li | Lin Jiang | Siyan Liu | Zeng Zhao | Minlie Huang | Changjie Fan | Zhipeng Hu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

There is a long history of research related to automated story generation, dating back as far as the 1970s. Recently, the rapid development of pre-trained language models has spurred great progresses in this field. Equipped with GPT-2 and the latest GPT-3, AI Dungeon has been seen as a famous example of the powerful text generation capabilities of large-scale pre-trained language models, and a possibility for future games. However, as a game, AI Dungeon lacks incentives to players and relies entirely on players to explore on their own. This makes players’ enthusiasm decline rapidly. In this paper, we present an open-ended text adventure game in Chinese, named as KuiLeiXi. In KuiLeiXi, players need to interact with the AI until the pre-determined plot goals are reached. By introducing the plot goals, players have a stronger incentive to explore ways to reach plot goals, while the AI’s abilities are not abused to generate harmful contents. This limited freedom allows this game to be integrated as a part of a romance simulation mobile game, Yu Jian Love. Since KuiLeiXi was launched, it has received a lot of positive feedbacks from more than 100,000 players. A demo video is available at https://youtu.be/DyYZhxMRrkk.