Papers by Yuqi Xue
Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model (2025.findings-naacl)
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| Challenge: | Recent large language models (LLMs) overlook children’s reading preferences, which poses challenges in CLA. |
| Approach: | They propose a method that augments large language models with children's reading preferences for adaptation by obtaining characters' personalities and narrative structure as additional information for fine-grained instruction tuning. |
| Outcome: | The proposed method significantly improves performance in automatic and human evaluation. |
CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations (2026.findings-acl)
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| Challenge: | LLM-empowered agent simulations generate rich, adaptive, and often nonlinear interaction patterns. |
| Approach: | They propose an automated Causal discovery framework for LLM agent simulations that converts mechanistic hypotheses into computable factors and learns a compact causal representation centered on an emergent target. |
| Outcome: | Experiments across four emergent settings demonstrate the promise of CAMO. |
TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks (2025.findings-acl)
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| Challenge: | Existing models with limited performance and limited training can be difficult to use in large-scale applications. |
| Approach: | They propose a training-free model routing method that optimizes synergy among multiple LLMs for open-domain text generation tasks. |
| Outcome: | The proposed method outperforms 13 baseline models and reduces costs by 17.20%. |