Papers by Jiayuan Wang
Detecting Conversational Mental Manipulation with Intent-Aware Prompting (2025.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to detect mental manipulations are limited due to complexity of detecting subtle, covert tactics in conversations. |
| Approach: | They propose an approach to detect mental manipulations using large language models using intent-aware prompting by capturing the intents of participants. |
| Outcome: | The proposed approach significantly reduces false negatives, helping detect more instances of mental manipulation with minimal misjudgment of positive cases. |
MMedAgent: Learning to Use Medical Tools with Multi-modal Agent (2024.findings-emnlp)
Copied to clipboard
Binxu Li, Tiankai Yan, Yuanting Pan, Jie Luo, Ruiyang Ji, Jiayuan Ding, Zhe Xu, Shilong Liu, Haoyu Dong, Zihao Lin, Yixin Wang
| Challenge: | Multi-modal Large Language Models (MLLMs) exhibit limited generality and often fall short when compared to specialized models. |
| Approach: | They propose a multi-modal medical agent that picks the most suitable medical tools based on user inputs. |
| Outcome: | The proposed agent performs better than open-source models and the closed-source model, GPT-4o. |
MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling (2025.findings-emnlp)
Copied to clipboard
| Challenge: | MT-RewardTree provides a framework for constructing, evaluating, and deploying process reward models in machine translation (MT) |
| Approach: | They propose a method for automatically generating token-level preference pairs using approximate Monte Carlo Tree Search. |
| Outcome: | The proposed framework achieves state-of-the-art performance in token-level evaluation and sequence-level analysis. |
Automatic Error Analysis for Document-level Information Extraction (2022.acl-long)
Copied to clipboard
| Challenge: | Document-level information extraction (IE) tasks have been revisited in earnest . evaluation of the approaches has been limited in a number of dimensions . |
| Approach: | They propose a transformation-based framework for automating error analysis in document-level event and (N-ary) relation extraction. |
| Outcome: | The proposed framework compares two state-of-the-art document-level template-filling approaches on datasets from three domains and four systems from the MUC-4 evaluation. |
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion? (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge graphs are far from complete with large portions of triplets missing. |
| Approach: | They propose to use Graph Neural Networks to learn powerful embeddings to improve model performance. |
| Outcome: | The proposed models achieve comparable performance to MLP models, suggesting that MP may not be as crucial as previously thought. |
A Virtual Assistant for Architectural Design in a VR Environment (2026.eacl-demo)
Copied to clipboard
Ander Salaberria, Oier Ijurco, Markel Ferro, Jiayuan Wang, Iñigo Vilá Muñoz, Roberto de Ioris, Jeremy Barnes, Oier Lopez De Lacalle
| Challenge: | Existing interfaces lack intuitive, contextaware capabilities needed for easily navigating complex architectural components in 3D spaces. |
| Approach: | They propose a virtual assistant that allows users to interact with the BIM file in a VR environment. |
| Outcome: | The proposed assistant could allow users to interact with the BIM file in a virtual reality environment and perform complex actions. |
Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement (2025.emnlp-industry)
Copied to clipboard
Haotan Guo, Jianfei He, Jiayuan Ma, Hongbin Na, Zimu Wang, Haiyang Zhang, Qi Chen, Wei Wang, Zijing Shi, Tao Shen, Ling Chen
| Challenge: | Phonetic Cloaking Replacement (PCR) is a problem in content moderation in China. |
| Approach: | They organize PCR into a four-way surface-form taxonomy and compile PCR-ToxiCN, a dataset of 500 phonetically cloaked offensive posts gathered from the RedNote platform. |
| Outcome: | The proposed model achieves only an F1-score and zero-shot chain-of-thought prompting pushes performance even lower. |
Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework (2026.acl-industry)
Copied to clipboard
Ziqiang Zhang, Jing Ma, Zilong Wang, Jiayuan Chen, Yi Qiao, Yu He, Wei Zhang, Dai Cheng, Xiaoyu Shen
| Challenge: | a production-grade pricing system for tourism is challenging due to unstructured nature of travel orders and ever-evolving pricing policies. |
| Approach: | They propose a production-grade pricing system with a strict decision boundary . they propose to combine structured extraction and bounded policy/path selection with interpretable condition trees . |
| Outcome: | The proposed system processed 3,960 orders in six months and reduced the order management team from 15-20 to 3 . the system reduced the per-order handling time from 10 minutes to 2 minutes. |
Knowledge-Guided Cross-Topic Visual Question Generation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for visual question generation use answers or question types as constraints to generate questions. |
| Approach: | They propose a knowledge-guided cross-topic visual question generation task to generate unseen topics in cross-section scenarios. |
| Outcome: | The proposed model outperforms baselines and can generate unseen topic-related questions in cross-topic scenarios. |
Language-Mediated, Object-Centric Representation Learning (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent work has studied the problem of unsupervised object representation learning, though without language. |
| Approach: | They propose language-mediated, Objectcentric Representation Learning (LORL) a paradigm for learning disentangled, objectcentric scene representations from vision and language. |
| Outcome: | The proposed paradigm improves performance of unsupervised object discovery algorithms on two datasets using language. |
M-TRACE: Detecting and Mitigating Time-Anchor Drift via Step-wise Conflict Checking in Temporal Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Experimental results show that M-TRACE effectively reduces time-anchor drift . external knowledge may be inaccurate while internal knowledge can become outdated . |
| Approach: | They propose a multi-agent reasoning framework for temporal knowledge conflicts . they propose 'TimeConfQA' which guides conflict-aware final reasoning . |
| Outcome: | Experimental results show that M-TRACE reduces time-anchor drift and improves performance on complex temporal question answering tasks. |
MatchTime: Towards Automatic Soccer Game Commentary Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing data on soccer commentary are often unsatisfactory, and the quality of existing data is often poor. |
| Approach: | They propose to manually annotate timestamps for 49 soccer matches and then use them to create a model to correct and filter existing data. |
| Outcome: | The proposed model improves the viewing experience of soccer and can be trained on the curated dataset. |
Fine-Grained Features-based Code Search for Precise Query-Code Matching (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods to locate code snippets from databases represent the semantics of code and query by averaging the features of each token and word. |
| Approach: | They propose a fine-grained code search model that consists of a cross-modal encoder, mapping layer and classification layer to capture fine-granular interactions between code and query. |
| Outcome: | The proposed model significantly outperforms existing methods across multiple programming language datasets. |
API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for quantifying uncertainty in large language models with black-box API access are limited due to the complex data distributions and inner model mechanism. |
| Approach: | They propose a conformal prediction method that minimizes the size of prediction sets and ensures a statistical guarantee of the user-defined coverage. |
| Outcome: | The proposed method outperforms existing methods on close-ended and open-ended questions. |
M-MAD: Multidimensional Multi-Agent Debate for Advanced Machine Translation Evaluation (2025.acl-long)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have shown their potential to deliver human-like judgments. |
| Approach: | They propose a systematic LLM-based multi-agent framework for advanced LLM as-a-judge MT evaluation that integrates dimension-specific results into a final evaluation judgment. |
| Outcome: | The proposed framework outperforms existing LLM-as-a-judge methods and competes with state-of-the-art automatic metrics even when powered by a suboptimal model like GPT-4o mini. |