Papers by Yanzhao Zhang
Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training (2024.lrec-main)
Copied to clipboard
| Challenge: | Pretrained language models (PLMs) have been successful in addressing word boundaries in Chinese sequence labeling tasks, but they rarely consider boundary information explicitly. |
| Approach: | They propose a method to integrate unsupervised boundary information into Chinese BERT's pre-training objectives and a supervised boundary-aware PLM. |
| Outcome: | The proposed model outperforms the vanilla version on Chinese sequence labeling tasks and in broader Chinese natural language understanding tasks. |
See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Brain CT report generation is important to aid physicians in diagnosing cranial diseases. |
| Approach: | They propose a Pathological Clue-driven Representation Learning model to build cross-modal representations based on pathological clues and adapt them for text generation. |
| Outcome: | The proposed method outperforms previous methods and achieves SoTA performance. |
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval (2024.emnlp-industry)
Copied to clipboard
Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li, Min Zhang
| Challenge: | Existing models for text retrieval are based on a multi-stage process that involves retrieving documents from a large corpus. |
| Approach: | They propose to build a multilingual text representation model and a cross-encoder reranker from scratch for text retrieval. |
| Outcome: | The proposed models outperform the state-of-the-art models on long-context retrieval benchmarks. |
Rethinking Composed Image Retrieval Evaluation: A Fine-Grained Benchmark from Image Editing (2026.acl-long)
Copied to clipboard
Tingyu Song, Yanzhao Zhang, Mingxin Li, Zhuoning Guo, Dingkun Long, Pengjun Xie, Siyue Zhang, Yilun Zhao, Shu Wu
| Challenge: | Composed Image Retrieval (CIR) is a complex task in multimodal understanding . current CIR benchmarks lack a robust evaluation pipeline and limited query categories . |
| Approach: | They construct a fine-grained CIR benchmark that allows for precise control over modification types and content. |
| Outcome: | The proposed benchmark covers 5,000 high-quality queries structured across five main categories and fifteen subcategories. |
A Two-Stage Adaptation of Large Language Models for Text Ranking (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in pre-trained language models (PLMs) have significantly improved ranking performance in text ranking tasks. |
| Approach: | They propose a two-stage progressive paradigm to better adapt LLMs to text ranking by conducting continual pre-training on a large weakly-supervised corpus and performing SFT on high-quality data. |
| Outcome: | The proposed approach outperforms previous methods on in- and out-domain scenarios. |
Granularity Matters: Pathological Graph-driven Cross-modal Alignment for Brain CT Report Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for automatic Brain CT reports are limited by coarse-grained supervision and coupled cross-modal alignment. |
| Approach: | They propose a pathological Graph-driven cross-modal alignment model that learns fine-grained visual cues and aligns them with textual words. |
| Outcome: | The proposed model can improve the automatic generation of Brain CT reports and contribute to improved cranial disease diagnosis. |
Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling (2022.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that Boundary-Aware BERT can improve Chinese sequence labeling tasks. |
| Approach: | They propose to encode boundary information directly into pre-trained language models . they propose to use unsupervised boundary information instead of supervised boundary info . |
| Outcome: | The proposed architecture improves Chinese sequence labeling tasks on ten benchmarks. |
Text Representation Distillation via Information Bottleneck Principle (2023.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) have recently shown great success in text representation field, however, the high computational cost and high-dimensional representation of PLMs pose significant challenges for practical applications. |
| Approach: | They propose a Knowledge Distillation method that distills large models into smaller representation models to reduce performance degradation after distillation. |
| Outcome: | Empirical results on two main downstream applications of the proposed method show that it reduces the risk of over-fitting and maximizes the mutual information between the model and the input data. |
Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Fine-tuning and in-context learning are two prevalent methods in imbuing large language models with task-specific knowledge. |
| Approach: | They propose to use a circuit shift theory to explain why in-context learning is superior to fine-tuning for tasks with implicit patterns. |
| Outcome: | The proposed method can grasp deep patterns and significantly improve accuracy on implicit patterns, compared with fine-tuning and in-context learning. |
Towards Text-Image Interleaved Retrieval (2025.acl-long)
Copied to clipboard
Xin Zhang, Ziqi Dai, Yongqi Li, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Meishan Zhang, Jun Yu, Wenjie Li, Min Zhang
| Challenge: | Existing multimodal information retrieval models rely on single-image inputs . current models use a dense retrieval paradigm, but this approach is not effective . |
| Approach: | They propose a text-image interleaved retrieval task where query and document are interleaves . they adapt off-the-shelf retrievers and build a dense baseline by interleaded multimodal large language model . |
| Outcome: | The proposed model achieves significant improvements over the baseline by substantially fewer visual tokens. |