Papers by Jinyi Hu
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating Attention (2022.emnlp-main)
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| Challenge: | Recent studies have shown that powerful Transformer architectures produce dull high-frequency phrases, severely hurting the diversity and novelty of generated text. |
| Approach: | They propose a method to control the sharpness of the attention distribution by python code and use it to learn a Bayesian approximation of posterior attention. |
| Outcome: | The proposed method improves diversity and novelty while maintaining comparable quality on conditional and unconditional generation tasks. |
OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems (2024.acl-long)
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Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, Maosong Sun
| Challenge: | Large Language Models (LLMs) and Large Multimodal Models have exceeded general human capabilities in various tasks. |
| Approach: | They present an Olympiad-level bilingual multimodal scientific benchmark featuring 8,476 problems from Olympiad level mathematics and physics competitions. |
| Outcome: | The best performing model, GPT-4V, attains an average score of 17.97% on OlympiadBench, with a mere 10.74% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning. |
GUICourse: From General Vision Language Model to Versatile GUI Agent (2025.acl-long)
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Wentong Chen, Junbo Cui, Jinyi Hu, Yujia Qin, Junjie Fang, Yue Zhao, Chongyi Wang, Jun Liu, Guirong Chen, Yupeng Huo, Yuan Yao, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Graphical User Interfaces (GUIs) are a pivotal medium for human-computer interaction. |
| Approach: | They propose a series of datasets for training visual-based GUI agents using general VLMs. |
| Outcome: | The proposed GUICourse datasets show that even a small-sized GUI agent performs better on GUI tasks. |
Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation (2022.findings-emnlp)
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| Challenge: | Variational Auto-Encoder (VAE) has been widely adopted in text generation due to its ability to learn flexible representations. |
| Approach: | They propose a Transformer-based recurrent VAE structure that imposes recurrence on segment-wise latent variables with arbitrarily separated text segments and constructs the posterior distribution with residual parameterization. |
| Outcome: | The proposed structure can deduce a non-zero lower bound of the KL term and enhance the entanglement of each segment and preceding latent variables, providing a theoretical guarantee of generation diversity. |
Generating Major Types of Chinese Classical Poetry in a Uniformed Framework (2020.lrec-1)
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| Challenge: | Chinese classical poetry is one of the most valuable literary and cultural heritages of China . it has many particular characteristics in its language structure, ranging from form, sound to meaning . a proposed uniformed framework for generating major types of Chinese classical poems is proposed . |
| Approach: | They propose a uniformed framework for generating major types of Chinese classical poems . they use a form- stressed weighting method to strengthen the control to the form of the generated poems a proposed framework is incorporated into Jiuge, the most influential Chinese classical poetry generation system developed by Tsinghua University. |
| Outcome: | The proposed framework can generate Chinese classical poems of major types with high quality in form and content. |
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation (2022.naacl-main)
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| Challenge: | Variational Auto-Encoders are often used for text generation tasks due to the sequential nature of the text. |
| Approach: | They propose a variational Transformer framework that learns a series of layer-wise latent variables with each inferred from those of lower layers and tightly coupled with the hidden states by low-rank tensor product. |
| Outcome: | The proposed framework can learn latent variables from lower layers and incorporate more information. |
LEGENT: Open Platform for Embodied Agents (2024.acl-demos)
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Zhili Cheng, Zhitong Wang, Jinyi Hu, Shengding Hu, An Liu, Yuge Tu, Pengkai Li, Lei Shi, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing integrations of large language models and large multimodal models are limited . Existing platforms for developing embodied agents are limited and limited based on open-source software. |
| Approach: | They propose an open platform for developing embodied agents using LLMs and LMMs. |
| Outcome: | The proposed platform surpasses GPT-4V in embodied tasks with its model training on LEGENT data. |