Papers by Jianyi Zhang
GLoCIM: Global-view Long Chain Interest Modeling for news recommendation (2025.coling-main)
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| Challenge: | Recent efforts to extract local subgraph information from click graphs have hindered collaboratively utilizing global click graph information. |
| Approach: | They propose a global-view long chain interests model that models a click graph with neighbor interest to enhance news recommendation. |
| Outcome: | The proposed method surpasses baseline methods on two real-world datasets. |
Revisiting Block-based Quantisation: What is Important for Sub-8-bit LLM Inference? (2023.emnlp-main)
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| Challenge: | Existing quantisation methods mainly focus on 8-bit LLMs . a lack of scaling offsets in the quantisation process limits the use of LLM inference. |
| Approach: | They propose to use block quantisations to reduce scaling offsets in Large language models . they find that the block quantizations reduce scaling only from an arithmetic perspective . |
| Outcome: | The proposed methods reduce scaling offsets solely from an arithmetic perspective without additional treatments in the computational path. |
Neuron Activation Modulation for Text Style Transfer: Guiding Large Language Models (2025.findings-acl)
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| Challenge: | Text style transfer (TST) aims to flexibly adjust the style of text while preserving its core content. |
| Approach: | They propose a method that aligns activation values of style-related neurons with those of the target style to guide the model in performing the transfer. |
| Outcome: | The proposed method significantly improves style transfer quality while preserving core content. |
Refining Salience-Aware Sparse Fine-Tuning Strategies for Language Models (2025.acl-long)
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| Challenge: | Existing methods for fine-tuning large language models require expensive training on consumer-grade hardwares. |
| Approach: | They propose a sparsity-based approach that introduces trainable sparse adaptations to the weight matrices in the model and offers greater flexibility in selecting fine-tuned parameters. |
| Outcome: | The proposed method outperforms other methods for a simple yet effective baseline for nLP tasks while sacrificing performance. |
ReAugKD: Retrieval-Augmented Knowledge Distillation For Pre-trained Language Models (2023.acl-short)
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Jianyi Zhang, Aashiq Muhamed, Aditya Anantharaman, Guoyin Wang, Changyou Chen, Kai Zhong, Qingjun Cui, Yi Xu, Belinda Zeng, Trishul Chilimbi, Yiran Chen
| Challenge: | Knowledge distillation (KD) is an effective compression technique to derive a smaller student model from a larger teacher model by transferring the knowledge embedded in the teacher's network. |
| Approach: | They propose a framework and loss function that preserves the semantic similarities of teacher and student training examples to enable the student to retrieve from the knowledge base effectively. |
| Outcome: | The proposed framework preserves the semantic similarities of teacher and student training examples to achieve state-of-the-art performance on the GLUE benchmark. |