Papers by Jingbo Zhou
Cross-layer Attention Sharing for Pre-trained Large Language Models (2026.tacl-1)
Copied to clipboard
Yongyu Mu, Yuzhang Wu, Yuchun Fan, Chenglong Wang, Hengyu Li, Jiali Zeng, Qiaozhi He, Murun Yang, Fandong Meng, Jie Zhou, Tong Xiao, Jingbo Zhu
| Challenge: | Existing studies focus on compressing the Key-Value cache or grouping attention heads, while overlooking redundancy between layers. |
| Approach: | They propose a lightweight substitute for self-attention in well-trained LLMs that uses feed-forward networks to align attention heads between adjacent layers and low-rank matrices to approximate differences in layer-wise attention weights. |
| Outcome: | The proposed model reduces redundancy by sharing weights across layers while maintaining high response quality while reducing redundant calculations within 53% 84% of the total layers. |
Multi-Path Transformer is Better: A Case Study on Neural Machine Translation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Extensive experiments on 12 WMT tasks show that shallower multi-path models can achieve similar or even better performance than the deeper model. |
| Approach: | They propose to use a parameter-efficient multi-path structure to fuse features extracted from different paths to achieve better performance. |
| Outcome: | The proposed model can achieve better performance with the same number of parameters than the deeper model. |
Joint Intent Detection and Entity Linking on Spatial Domain Queries (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Spatial domain queries have unique properties making them more challenging for language understanding than common conversational queries. |
| Approach: | They propose a language understanding framework for spatial domain queries that jointly learns the intent detection and entity linking tasks on a voice assistant service. |
| Outcome: | The proposed framework outperforms baseline methods with a significant margin. |
A Table-to-Text Framework with Heterogeneous Multidominance Attention and Self-Evaluated Multi-Pass Deliberation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Table-to-text works have been widely applied in different domains, such as weather forecast and financial report generation. |
| Approach: | They propose a table-to-text approach on top of Self-evaluated multi-pass Generation and Heterogenous Multidominance Attention to explore the hierarchical structure. |
| Outcome: | The proposed method outperforms several SOTA methods quantitatively and qualitatively on three public datasets. |
Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach (2022.emnlp-main)
Copied to clipboard
| Challenge: | Currently, the generalization issues hinder the applicability of neural table-to-text models due to the limited source tables. |
| Approach: | They propose a table-structureaware text generation model with pretrained language model and propose TASD to bridge the gap between the structured table and text input. |
| Outcome: | The proposed model bridges the gap between the structured table and text input and generates accurate and fluent descriptive texts on two public datasets. |
AAPO: Enhancing the Reasoning Capabilities of LLMs with Advantage Margin (2026.acl-long)
Copied to clipboard
| Challenge: | Reinforcement learning (RL) has emerged as an effective approach for enhancing the reasoning capabilities of large language models. |
| Approach: | They propose an algorithm that optimizes cross-entropy loss using advantages enhanced through a margin-based estimation scheme. |
| Outcome: | Experimental results show that AAPO improves group relative advantage estimation compared to other methods. |
An Overview of the Active Gene Annotation Corpus and the BioNLP OST 2019 AGAC Track Tasks (D19-57)
Copied to clipboard
| Challenge: | Biomedical natural language processing (BioNLP) has long been recognized as effective method to accelerate drug-related knowledge discovery. |
| Approach: | They developed an active gene annotation corpus (AGAC) to support drug repurposing. |
| Outcome: | The active gene annotation corpus (AGAC) was developed to support knowledge discovery for drug repurposing. |
A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (2022.emnlp-main)
Copied to clipboard
Yuan Xia, Zhenhui Shi, Jingbo Zhou, Jiayu Xu, Chao Lu, Yehui Yang, Lei Wang, Haifeng Huang, Xia Zhang, Junwei Liu
| Challenge: | With the development of medical digitization, the extraction and structuring of electronic medical records (EMRs) have become challenging but fundamental tasks. |
| Approach: | They propose a speaker-aware dialogue encoder with multi-task learning which takes the speaker's identity into account and a co-attention fusion network to aggregate the utterance information. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the public medical dialogue extraction datasets to demonstrate its superiority. |
Visualization Recommendation with Prompt-based Reprogramming of Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Traditional visualization recommendations require extensive manual maintenance and yet fail to fully comprehend tabular data. |
| Approach: | They propose a hierarchical table prompt-based reprogramming framework that integrates tabular data into LLMs through a strategically crafted prompt learning method. |
| Outcome: | The proposed framework achieves state-of-the-art performance and will be made publicly available upon acceptance. |
Evaluating the Smooth Control of Attribute Intensity in Text Generation with LLMs (2024.findings-acl)
Copied to clipboard
| Challenge: | Controllable text generation is increasingly tailored to individual preferences. |
| Approach: | They propose to evaluate the attribute intensity of text generated by large language models on five different attributes for error, variation of the generated sentence's intensities and relevance to the generation questions. |
| Outcome: | The proposed methods are based on Elo rating system and GPT4 and are able to be trained without training. |
Deriving Character Logic from Storyline as Codified Decision Trees (2026.acl-long)
Copied to clipboard
| Challenge: | Existing behavioral profiles are unstructured, weakly validated, and unusable . existing models are weakly valid, leading to brittle agent behavior . Using codified decision trees, we show that CDT outperforms previous methods . |
| Approach: | They propose a data-driven framework that induces an executable decision structure from narrative data. |
| Outcome: | The proposed framework outperforms human-written profiles and prior profiles on multiple benchmarks. |
Point-of-Interest Oriented Question Answering with Joint Inference of Semantic Matching and Distance Correlation (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing methods for POI oriented question answering lack ability to handle important POI related information. |
| Approach: | They propose a deep learning framework integrated with joint inference to capture tag semantic and geographic correlation between question and POIs. |
| Outcome: | The proposed model captures both tag semantic and geographic correlation between question and POIs. |
DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition (2026.acl-industry)
Copied to clipboard
| Challenge: | Accurate Point of Interest (POI) attribute acquisition is essential for location-based services, yet traditional IVR systems suffer from error accumulation and high maintenance overhead. |
| Approach: | They propose a large language model-based framework for large-scale POI attribute acquisition at Baidu Maps. |
| Outcome: | The proposed framework outperforms existing IVR systems in 83.9% task success rate while maintaining a low reaction time of 130ms. |