Papers by Jingwei Yu
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools (2023.emnlp-demo)
Copied to clipboard
Jingwei Ni, Julia Bingler, Chiara Colesanti-Senni, Mathias Kraus, Glen Gostlow, Tobias Schimanski, Dominik Stammbach, Saeid Ashraf Vaghefi, Qian Wang, Nicolas Webersinke, Tobias Wekhof, Tingyu Yu, Markus Leippold
| Challenge: | a lack of transparency in sustainability reporting is a key challenge due to the sheer volume and complexity of sustainability reports . only a few entities worldwide have the resources to analyze these reports at scale . a novel LLM-based system to automate the analysis of corporate sustainability reports is needed . |
| Approach: | They propose a novel LLM-based system to automate the analysis of corporate sustainability reports. |
| Outcome: | The proposed system automates the analysis of corporate sustainability reports. |
Introducing Graph Context into Language Models through Parameter-Efficient Fine-Tuning for Lexical Relation Mining (2025.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models can effectively mine lexical relations between word pairs . however, graph features and semantic knowledge of pre-tried models are lacking in the task. |
| Approach: | They propose a parameter-efficient fine-tuning method which integrates graph features and semantic representations for lexical relation classification and lexic entailment tasks. |
| Outcome: | The proposed method integrates graph features and semantic representations for lexical relation mining tasks. |
MultiCMET: A Novel Chinese Benchmark for Understanding Multimodal Metaphor (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing research on multimodal metaphors does not address categorizing the source and target domains in metaphors beyond the English language. |
| Approach: | They propose a Cascading Domain Knowledge Integration benchmark to detect metaphors by introducing domain-specific lexical features. |
| Outcome: | The proposed dataset includes 13,820 text-image pairs of advertisements with manual annotations of the occurrence of metaphors, domain categories, and sentiments metaphors convey. |
Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing work fine tunes the PLM with the news recommendation task, which can cause a domain shift problem. |
| Approach: | They propose a self-supervised method to adapt general PLM to news domain with a contrastive matching task between news titles and news bodies. |
| Outcome: | The proposed method can improve both the effectiveness and efficiency of the large PLM-based news recommendation model while maintaining its performance. |
SRF: Enhancing Document-Level Relation Extraction with a Novel Secondary Reasoning Framework (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for document-level relation extraction ignore bidirectional mention interaction when generating relational features for entity pairs. |
| Approach: | They propose a document-level relation extraction model that incorporates bidirectional mention fusion and a simple yet effective evidence extraction module for relation prediction. |
| Outcome: | The proposed model achieves SOTA performance and the proposed method is effective and general when integrated into existing models. |
Can Reasoning Help Large Language Models Capture Human Annotator Disagreement? (2026.eacl-long)
Copied to clipboard
Jingwei Ni, Yu Fan, Vilém Zouhar, Donya Rooein, Alexander Miserlis Hoyle, Mrinmaya Sachan, Markus Leippold, Dirk Hovy, Elliott Ash
| Challenge: | Variation in human annotation (i.e., disagreements) is common in NLP, but it is unclear whether it is possible to model this variation in LLMs. |
| Approach: | They evaluate the influence of different reasoning settings on LLM disagreement modeling . RLVR-style reasoning degrades performance in disagreement modeling, they find . |
| Outcome: | The proposed reasoning settings improve LLM disagreement modeling, while RLVR-style reasoning degrades it. |
Entity Pair-guided Relation Summarization and Retrieval in LLMs for Document-level Relation Extraction (2025.findings-naacl)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) aims to extract relations between entities in a document. |
| Approach: | They propose an entity pair-guided relation summarization and retrieval model for DocRE . the model uses entity pairs to guide relation summaries and retrievals . |
| Outcome: | The proposed model achieves state-of-the-art (SOTA) performance on three datasets. |
Co-DETECT: Collaborative Discovery of Edge Cases in Text Classification (2025.emnlp-demos)
Copied to clipboard
Chenfei Xiong, Jingwei Ni, Yu Fan, Vilém Zouhar, Donya Rooein, Lorena Calvo-Bartolomé, Alexander Miserlis Hoyle, Zhijing Jin, Mrinmaya Sachan, Markus Leippold, Dirk Hovy, Mennatallah El-Assady, Elliott Ash
| Challenge: | Social scientists often need to develop codebooks that can be reliable but require significant human effort. |
| Approach: | They propose a mixed-initiative annotation framework that integrates human expertise with automatic annotation guided by large language models. |
| Outcome: | The proposed framework integrates human expertise with automatic annotation guided by large language models. |
"Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborations (2026.acl-long)
Copied to clipboard
| Challenge: | Existing large language models (LLMs) are reactive and respond only when prompted, limiting their effectiveness in collaborative settings. |
| Approach: | They introduce a proactive LLM assistant designed to enhance biomedical collaboration between AI systems and human experts through timely, context-aware interventions. |
| Outcome: | The proposed model outperforms baselines in intervention precision and collaborative task utility, highlighting the potential of proactive LLMs as intelligent scientific assistants. |
Measuring Human Contribution in AI-Assisted Content Generation (2026.acl-long)
Copied to clipboard
Yueqi Xie, Tao Qi, Jingwei Yi, Xiyuan Yang, Ryan Whalen, Junming Huang, Qian Ding, Yu Xie, Xing Xie, Fangzhao Wu
| Challenge: | generative AI has created a new way to generate content with humans . varying degrees of human contribution in content generation poses significant challenges for the delineation of originality . |
| Approach: | They propose a framework to measure human contribution in AI-assisted content generation by calculating mutual information between human input and AI-aided output relative to self-information of AI-assist output. |
| Outcome: | The proposed measure discriminates between varying degrees of human contribution across multiple creative domains and is validated in real-world applications. |
ONSEP: A Novel Online Neural-Symbolic Framework for Event Prediction Based on Large Language Model (2024.findings-acl)
Copied to clipboard
| Challenge: | TKGF is a technique that requires experience during testing and relying on a single short-term history. |
| Approach: | They propose a framework that integrates dynamic causal rule mining and dual history augmented generation to enhance event prediction. |
| Outcome: | The proposed framework shows significant performance improvements across diverse datasets and significantly improves Hit@k. |
Rethinking the Role of LLMs for Document-level Relation Extraction: a Refiner with Task Distribution and Probability Fusion (2025.naacl-long)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) provides a broad context for extracting relations for entities. |
| Approach: | They propose a method that utilizes LLMs as a refiner and task distribution and probability fusion to refine LLM-based relation extraction methods. |
| Outcome: | The proposed method outperforms existing LLM-based methods without fine-tuning by 25.2% F1. |