Papers by Wenjie Dong
Beyond Single Frames: Can LMMs Comprehend Implicit Narratives in Comic Strip? (2025.findings-emnlp)
Copied to clipboard
Xiaochen Wang, Heming Xia, Jialin Song, Longyu Guan, Qingxiu Dong, Rui Li, Yixin Yang, Yifan Pu, Weiyao Luo, Yiru Wang, Xiangdi Meng, Wenjie Li, Zhifang Sui
| Challenge: | Large Multimodal Models have demonstrated strong performance on vision-language benchmarks, yet current evaluations focus on single-image reasoning. |
| Approach: | STRIPCIPHER is a benchmark designed to evaluate model ability on understanding implicit narratives in silent comics. |
| Outcome: | STRIPCIPHER is a high-quality, human-annotated dataset featuring fine-grained annotations and comprehensive coverage of varying difficulty levels. |
Multimodal Cross-lingual Phrase Retrieval (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to cross-lingual phrase retrieval only deal with textual modality, leaving the question of the effectiveness of using multimodal information unanswered. |
| Approach: | They propose a multimodal cross-lingual phrase retrieval resource that integrates a Wikimedia Commons media store and a large multimodal pre-trained model to bridge the gap between different modalities. |
| Outcome: | The proposed approach performs significantly better than pure textual cross-lingual phrase retrieval on a benchmarked dataset covering eight language pairs. |
Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding (2024.findings-acl)
Copied to clipboard
Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, Zhifang Sui
| Challenge: | Large Language Models (LLMs) have a high inference latency stemming from autoregressive decoding. |
| Approach: | They propose a novel decoding paradigm that drafts multiple tokens and verifies them in parallel . they aim to provide a catalyst for further research on Speculative Decoding . |
| Outcome: | The proposed method drafts multiple tokens and verifies them in parallel . it can be used to accelerate inference in large language models. |
A Survey on Open Information Extraction from Rule-based Model to Large Language Model (2024.findings-emnlp)
Copied to clipboard
Liu Pai, Wenyang Gao, Wenjie Dong, Lin Ai, Ziwei Gong, Songfang Huang, Li Zongsheng, Ehsan Hoque, Julia Hirschberg, Yue Zhang
| Challenge: | Open Information Extraction (OpenIE) is a key NLP task aimed at extracting structured information from unstructured text sources. |
| Approach: | They propose to categorize OpenIE into rule-based, neural, and pre-trained large language models and discuss each within a chronological framework. |
| Outcome: | The paper categorizes OpenIE approaches into rule-based, neural, and pre-trained large language models, discussing each within a chronological framework. |
ProTOD: Proactive Task-oriented Dialogue System Based on Large Language Model (2025.coling-main)
Copied to clipboard
| Challenge: | Existing task-oriented dialogue systems engage with users in a reactive manner, relying on a basic single-query mechanism and employing passive policy planning. |
| Approach: | They propose a novel LLM-based proactive TOD framework to improve system proactivity and goal completion. |
| Outcome: | The proposed framework improves system proactivity and goal completion rates by 10% while enhancing proactive engagement. |
How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have focused on dialogue simulation while overlooking human behavior simulation, which is crucial for digital twins. |
| Approach: | They propose to integrate persona metadata into LLMs and use it to iteratively infer contextually appropriate behaviors within dynamic scenarios. |
| Outcome: | The proposed model is based on 15,846 distinct behaviors across 1,001 unique personas and incorporates persona metadata to iteratively infer appropriate behaviors within dynamic scenarios. |