Papers by Lianwen Jin
TongGu: Mastering Classical Chinese Understanding with Knowledge-Grounded Large Language Models (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capability in Natural Language Processing (NLP), but struggle with Classical Chinese Understanding (CCU) Existing models, including general-purpose and preliminary LLMs, lack the ability to address CCU in data-demanding and knowledge-intensive tasks. |
| Approach: | They propose to use a classical Chinese corpora-based instruction-tuning dataset to unlock the full CCU potential of LLMs. |
| Outcome: | The proposed model unlocks the full CCU potential of LLMs by preserving its foundational knowledge while maintaining redundancy-aware tuning (RAT) and CCU-RAG. |
VideoCLIP-XL: Advancing Long Description Understanding for Video CLIP Models (2024.emnlp-main)
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| Challenge: | Existing studies have shown that CLIP models with short summary texts cannot process extensive textual descriptions due to its text encoder's reliance on positional embeddings with length 77. |
| Approach: | They propose a Contrastive Language-Image Pre-training (CLIP) model which aims to unleash the long-description understanding capability of video CLIP models. |
| Outcome: | The proposed model can learn the distribution of feature space while expanding the long description capability. |
HisDoc-OCR: Restoring Visual Grounding in MLLMs for Chinese Historical Document OCR (2026.findings-acl)
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| Challenge: | Despite multimodal large language models' strong performance on modern document OCR, their application to historical Chinese texts suffers from severe hallucinations, character fabrication, uncontrolled repetition, and semantic drift. |
| Approach: | They propose a multimodal large language model which restores visual grounding through three synergistic strategies: Layout Injection, First-Occurrence Boost, Self-Distilled Attention Focusing and HisDoc-OCR. |
| Outcome: | The proposed model outperforms general-purpose and OCR-specific models on Chinese historical documents. |
LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding (2022.acl-long)
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| Challenge: | Existing structured document understanding models only deal with document data of specific language(s) this is extremely limited for other languages, especially in the case of lacking pre-training structured document data. |
| Approach: | They propose a language-independent Layout Transformer (LiLT) for structured document understanding . they propose to pre-train structured documents in a single language and fine-tune them on other languages . |
| Outcome: | The proposed model achieves competitive or even superior performance on diverse downstream benchmarks on eight languages. |
Rapid Diffusion: Building Domain-Specific Text-to-Image Synthesizers with Fast Inference Speed (2023.acl-industry)
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Bingyan Liu, Weifeng Lin, Zhongjie Duan, Chengyu Wang, Wu Ziheng, Zhang Zipeng, Kui Jia, Lianwen Jin, Cen Chen, Jun Huang
| Challenge: | Text-to-Image Synthesis (TIS) aims to generate images based on textual inputs . but, current diffusion-based models lack entity knowledge and low inference speed . |
| Approach: | They propose a framework for training and deploying latent diffusion models with rich entity knowledge injected and optimized networks. |
| Outcome: | The proposed framework improves image quality and inference speed and can be used in industrial applications. |
Large-Scale Corpus Construction and Retrieval-Augmented Generation for Ancient Chinese Poetry: New Method and Data Insights (2025.findings-naacl)
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| Challenge: | Ancient Chinese poetry presents unique challenges for Large Language Models due to data scarcity and limited ability of general LLMs when dealing with ACP. |
| Approach: | They propose a specialized Retrieval-Augmented Generation framework to improve LLMs' performance . they use 1.1 million ancient poems and 990K related texts to address hallucination issues . |
| Outcome: | The proposed framework improves performance of LLMs in ancient Chinese poetry domain from 49.2% to 89.0%. |
DiffChat: Learning to Chat with Text-to-Image Synthesis Models for Interactive Image Creation (2024.findings-acl)
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| Challenge: | a novel method to align Large Language Models to "chat" with prompt-as-input Text-to-Image Synthesis models is proposed . a user-specified instruction can be used to create a high quality image . |
| Approach: | They propose a method to align Large Language Models to "chat" with prompt-as-input Text-to-Image Synthesis models for interactive image creation. |
| Outcome: | The proposed method can exhibit superior performance than baseline models and strong competitors based on automatic and human evaluations. |
Draft, Verify, Restore: Self-Refining Historical Inscription Restoration with a Unified MLLM (2026.acl-long)
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| Challenge: | Existing methods for end-to-end historical inscription restoration rely on task-separated pipelines with irreversible error accumulation and patch-based generation that sacrifices page-level consistency. |
| Approach: | They propose a unified MLLM for end-to-end historical inscription restoration that integrates draft-guided localization and Hierarchical self-refinement to enable accurate damage localization. |
| Outcome: | The proposed model achieves superior performance in both text restoration accuracy and appearance restoration quality. |
Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration (2025.acl-long)
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Yuyi Zhang, Peirong Zhang, Zhenhua Yang, Pengyu Yan, Yongxin Shi, Pengwei Liu, Fengjun Guo, Lianwen Jin
| Challenge: | Existing methods for historical document restoration focus on single modality or limited-size restoration, failing to meet practical needs. |
| Approach: | They propose a full-page HDR dataset and an automated HDR solution to replace manual restoration methods. |
| Outcome: | The proposed solution improves OCR accuracy from 46.83% to 84.05% when processing severely damaged documents, with enhancement to 94.25% through human-machine collaboration. |
PPTSER: A Plug-and-Play Tag-guided Method for Few-shot Semantic Entity Recognition on Visually-rich Documents (2024.findings-acl)
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| Challenge: | Existing methods for visually-rich document information extraction are limited . Xu et al., 2020: visually rich document information is a vital aspect of document understanding . |
| Approach: | They propose a plug-and-play Tag-guided method for few-shot Semantic Entity Recognition (PPTSER) on visually-rich documents. |
| Outcome: | The proposed method outperforms fine-tuning and few-shot methods on visual-rich documents. |
CocaCLIP: Exploring Distillation of Fully-Connected Knowledge Interaction Graph for Lightweight Text-Image Retrieval (2023.acl-industry)
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| Challenge: | Existing methods for text-image retrieval are limited to edge devices and real-time situations due to the substantial indexing and inference time. |
| Approach: | They propose a fully-Connected knowledge interaction graph technique for cross-modal pre-training distillation. |
| Outcome: | The proposed method achieves SOTA performances on the widely-used Flickr30K and MSCOCO benchmarks under the lightweight setting. |
MCS-Bench: A Comprehensive Benchmark for Evaluating Multimodal Large Language Models in Chinese Classical Studies (2025.acl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) have advanced visual and language understanding, but their potential in Chinese Classical Studies (CCS) remains underexplored due to the lack of specialized benchmarks. |
| Approach: | They propose to develop a multimodal benchmark specifically designed for Chinese Classical Studies across multiple subdomains to bridge this gap. |
| Outcome: | The proposed benchmark spans seven core subdomains with a total of 45 meticulously designed tasks. |
RedundancyLens: Revealing and Exploiting Visual Token Processing Redundancy for Efficient Decoder-Only MLLMs (2025.findings-acl)
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| Challenge: | Current decoder-only architectures achieve higher performance but lower efficiency . cross-attention-based architectures skip visual token computations . |
| Approach: | They propose a training-free framework for analyzing trained MLLMs to investigate redundancy . they propose 'probe-activated Dynamic FFN and Hollow Attention' algorithms for visual token reductions and a layer ranking algorithm for inference acceleration. |
| Outcome: | The proposed framework achieves comparable performance to or better than state-of-the-art methods while remaining compatible with them. |