Papers by Xingjian He
Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models (2025.acl-long)
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Xinlin Zhuang, Jiahui Peng, Ren Ma, Yinfan Wang, Tianyi Bai, Xingjian Wei, Qiu Jiantao, Chi Zhang, Ying Qian, Conghui He
| Challenge: | composition of pre-training datasets for large language models remains undisclosed . current methods for evaluating data quality are limited by single-dimensional evaluation or redundancy-focused strategies. |
| Approach: | They propose a multi-dimensional data selection method that integrates dimensions with existing quality metrics through learned optimal weightings. |
| Outcome: | The proposed method doubles convergence speed for 1.3B model models and improves downstream task performance by 3.23%. |
RWKV: Reinventing RNNs for the Transformer Era (2023.findings-emnlp)
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Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu
| Challenge: | recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability. |
| Approach: | They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. |
| Outcome: | The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models. |
Beyond Literal Descriptions: Understanding and Locating Open-World Objects Aligned with Human Intentions (2024.findings-acl)
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| Challenge: | Existing methods for visual grounding rely on the assumption that the given expression must be literal . this impedes the practical deployment of agents in real-world scenarios. |
| Approach: | They propose a visual grounding task that uses intention expressions to locate foreground entities . they build a large-scale IVG dataset with free-form intention expression to promote VG . |
| Outcome: | The proposed method is based on a large-scale intention-driven visual-language (V-L) dataset with free-form intention expressions. |
OpenHuEval: Evaluating Large Language Model on Hungarian Specifics (2025.findings-acl)
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Haote Yang, Xingjian Wei, Jiang Wu, Noémi Ligeti-Nagy, Jiaxing Sun, Yinfan Wang, Győző Zijian Yang, Junyuan Gao, Jingchao Wang, Bowen Jiang, Shasha Wang, Nanjun Yu, Zihao Zhang, Shixin Hong, Hongwei Liu, Wei Li, Songyang Zhang, Dahua Lin, Lijun Wu, Gábor Prószéky, Conghui He
| Challenge: | Recent advances in Large Language Models (LLMs) represent significant strides toward artificial general intelligence (AGI). |
| Approach: | They introduce OpenHuEval, the first benchmark for LLMs focusing on the Hungarian language and specifics. |
| Outcome: | The framework reveals intrinsic patterns and mechanisms of LLMs in non-English languages, with Hungarian serving as an example. |
Dive into Deep Learning for Natural Language Processing (D19-2)
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| Challenge: | GluonNLP is a powerful new toolkit that automates the most laborious aspects of deep learning for NLP. |
| Approach: | This hands-on tutorial demonstrates how to scale unsupervised pre-training techniques with Apache MXNet and GluonNLP. |
| Outcome: | This hands-on tutorial examines the challenges of scaling these models and algorithms effectively with Apache MXNet and GluonNLP. |
ViPE: Visual Perception in Parameter Space for Efficient Video-Language Understanding (2025.emnlp-main)
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| Challenge: | Existing video-language models rely on concatenating visual tokens with textual inputs for joint modeling, but this method suffers from significant inefficiency when scaling to long videos with dense visual inputs. |
| Approach: | They propose a video-to-parameter efficiency paradigm called ViPE that transforms video content into visual perceptual weights, which are directly injected into the LLM’s parameters. |
| Outcome: | The proposed model reduces FLOPs by 85% and inference time by up to 65% while reducing FLOP and FLOP inference times by up-to-65%. |