Papers by Runze Xia
CrowdAgent: Multi-Agent Managed Multi-Source Annotation System (2025.emnlp-demos)
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Maosheng Qin, Renyu Zhu, Mingxuan Xia, null Chenchenkai, Zhen Zhu, Minmin Lin, Junbo Zhao, Lu Xu, Changjie Fan, Runze Wu, Haobo Wang
| Challenge: | Recent approaches to annotate data focus on labeling, but lack holistic process control . a novel system that integrates task assignment, data annotation, and quality/cost management is needed . |
| Approach: | They propose a multi-agent system that integrates task assignment, data annotation, and quality/cost management. |
| Outcome: | The proposed system automates human management by using a collaborative multi-agent system. |
Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation (2025.acl-long)
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| Challenge: | Existing methods for data annotation use an aggressive approach prompting LLMs to determine a single gold label for each unlabeled sample. |
| Approach: | They propose a teacher-student framework that distills candidate annotations with a Small Language Model (SLM) they propose to use LLMs to generate and distill candidate annotation with slms to ensure unique labels are provided for downstream tasks. |
| Outcome: | The proposed method outperforms existing methods due to uncertainty in LLMs and is noisetolerant. |
Decoding the Echoes of Vision from fMRI: Memory Disentangling for Past Semantic Information (2024.emnlp-main)
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| Challenge: | Experimental results demonstrate that this method effectively disentangles the information within fMRI signals. |
| Approach: | They propose a task Memory Disentangling which extracts and decodes past information from fMRI signals. |
| Outcome: | The proposed method extracts and decodes past information from fMRI signals. |
DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language Models (2026.acl-long)
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| Challenge: | Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs). |
| Approach: | They propose a framework that explicitly decouples FFN and Attention contributions via noise-induced causal interventions to capture model's internal fragility. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in both uncertainty estimation and calibration while exhibiting superior cross-dataset generalization. |