Papers by Wenjia Zhang
NarrativePlay: Interactive Narrative Understanding (2024.eacl-demo)
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| Challenge: | Existing systems for interactive agents focus on specific capabilities in predetermined scenarios. |
| Approach: | They propose a novel system that allows users to role-play a fictional character and interact with other characters in narratives in an immersive environment. |
| Outcome: | The proposed system generates human-like responses guided by personality traits extracted from narratives. |
Tag-Instruct: Controlled Instruction Complexity Enhancement through Structure-based Augmentation (2025.findings-acl)
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| Challenge: | High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity. |
| Approach: | They propose a framework that compresses instructions into a compact tag space and enhances complexity through RL-guided tag expansion. |
| Outcome: | The proposed framework outperforms existing methods in the evaluation of instruction complexity augmentation and semantic compression of text into a compact tag space. |
PlanGPT: Enhancing Urban Planning with a Tailored Agent Framework (2025.acl-industry)
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| Challenge: | Empirical tests demonstrate that PlanGPT framework has achieved advanced performance, providing comprehensive support that significantly enhances professional planning efficiency. |
| Approach: | They propose a specialized AI agent framework tailored for urban and spatial planning that integrates a customized local database retrieval system and domain-specific knowledge activation capabilities. |
| Outcome: | Empirical tests show that PlanGPT framework significantly improves planning efficiency . it integrates a customized database retrieval system, domain-specific knowledge activation capabilities, and advanced tool orchestration mechanisms. |
PlanGPT-VL: Enhancing Urban Planning with Domain-Specific Vision-Language Models (2025.emnlp-industry)
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| Challenge: | Existing Vision-Language Models (VLMs) fail to analyze planning maps . specialized visual representations of land use zones, transportation networks, and development policies are needed to interpret complex planning maps. |
| Approach: | They propose a domain-specific VLM tailored for urban planning maps that employs three innovations: PlanAnno-V framework for high-quality VQA data synthesis, Critical Point Thinking (CPT) and PlanBench-V benchmark for systematic evaluation. |
| Outcome: | The new model outperforms general-purpose VLMs on planning map interpretation tasks. |
Visual Prompting in LLMs for Enhancing Emotion Recognition (2024.emnlp-main)
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Qixuan Zhang, Zhifeng Wang, Dylan Zhang, Wenjia Niu, Sabrina Caldwell, Tom Gedeon, Yang Liu, Zhenyue Qin
| Challenge: | Existing methods for enhancing in-context emotion classification fail to include spatial relationships between different people and facial features within a single face. |
| Approach: | They propose a set-of-vision prompting approach that uses spatial information to mark targets precisely. |
| Outcome: | The proposed approach improves face count and emotion categorization while preserving the enriched image context. |
FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only (2025.findings-acl)
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| Challenge: | Recent studies explore approaches to synthesize instruction data with open-sourced LLMs but require high-quality human-crafted seed data. |
| Approach: | They propose an end-to-end framework to synthesize high-quality instruction data with open-sourced LLMs and sampled unlabeled documents, eliminating the need for seed data. |
| Outcome: | The proposed framework synthesizes high-quality instruction data with open-sourced LLMs and sampled unlabeled documents, eliminating the need for seed data. |
SQLAgent: Learning to Explore Before Generating as a Data Engineer (2026.findings-acl)
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| Challenge: | Existing approaches to large language models fail to generalize in complex, real-world settings due to database-specific nature of SQL reasoning. |
| Approach: | They propose a two-stage LLM-based framework that decouples knowledge acquisition from query generation. |
| Outcome: | The proposed framework significantly improves accuracy over baselines on large-scale benchmarks. |