Papers by Linjuan Wu
A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM𝛥 Integration into Upcycled MoE (2026.acl-long)
Copied to clipboard
Hao Zhou, Tianhao Li, Zhijun Wang, Shuaijie She, Linjuan Wu, Hao-Ran Wei, Baosong Yang, Jiajun Chen, Shujian Huang
| Challenge: | Large Language Models (LLMs) are expensive and require extensive Continued Pre-Training and data-intensive alignment to expand. |
| Approach: | They propose a method which upcycles a dense model into a Mixture-of-Experts architecture, allocating different experts to different languages. |
| Outcome: | Experiments show that the proposed model upcycles a dense model into a Mixture-of-Experts(MoE) architecture, allocating different experts to different languages. |
Pause or Fabricate? Training Language Models for Grounded Reasoning (2026.findings-acl)
Copied to clipboard
Yiwen Qiu, Linjuan Wu, Yizhou Liu, Yuchen Yan, Jin Ma, Xu Tan, Yao Hu, Daoxin Zhang, Wenqi Zhang, Weiming Lu, Jun Xiao, Yongliang Shen
| Challenge: | Large language models implicitly fabricate information when inputs are incomplete, causing confidence but unreliable conclusions. |
| Approach: | They propose a framework for grounded reasoning under incomplete information that decomposes reasoning into two stages . they propose stage-specific rewards to penalize hallucinations, enabling models to detect gaps, stop proactively, and resume reasoning after clarification. |
| Outcome: | The proposed framework improves premise detection and task success by 30% . it also reduces average response length by over 20% . |
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods to zero-shot transfer knowledge from rich-resource to low-resourced languages are limited due to linguistic discrepancies in different languages. |
| Approach: | They propose a multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model to disassociate semantics from syntax in models learned by multilingual pre-trained models. |
| Outcome: | The proposed model disassociates semantics from syntax in multilingual models. |
AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification (2025.emnlp-main)
Copied to clipboard
Xuan Zhang, Yongliang Shen, Zhe Zheng, Linjuan Wu, Wenqi Zhang, Yuchen Yan, Qiuying Peng, Jun Wang, Weiming Lu
| Challenge: | Existing tools for ambiguous and incomplete queries are limited by manual construction and lack of error correction mechanisms during multi-turn clarification. |
| Approach: | They propose a framework that exploits the mapping between queries and their tool invocation solutions by removing key parameters from queries while retaining them as ground truth. |
| Outcome: | The proposed framework outperforms existing methods while maintaining high accuracy in tool invocation. |
Good Meta-tasks Make A Better Cross-lingual Meta-transfer Learning for Low-resource Languages (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Model-agnostic meta-learning has garnered attention as a promising technique for enhancing few-shot cross-lingual transfer learning in low-resource scenarios. |
| Approach: | They propose a Meta-Task Collector-based Cross-lingual Meta-Transfer framework to adapt data selection strategies to construct cross-lingual meta-tasks to reduce language gaps. |
| Outcome: | The proposed framework significantly improves model performance in the target language with minimal annotation costs. |
Enhancing LLM Language Adaption through Cross-lingual In-Context Pre-training (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for enhancing cross-lingual transfer are limited by parallel resources and lack linguistic and domain coverage. |
| Approach: | They propose a cross-lingual in-context pre-training approach that leverages semantically related bilingual Wikipedia documents to enhance cross-linguistic transfer. |
| Outcome: | The proposed approach improves multilingual performance on three models across six target languages. |
From English to Second Language Mastery: Enhancing LLMs with Cross-Lingual Continued Instruction Tuning (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) acquire strong language skills through extensive pre-training and supervised fine-tuning (SFT) on instructionresponse pairs. |
| Approach: | They propose a method which leverages translation-based parallel instruction data to enhance cross-lingual adaptability. |
| Outcome: | The proposed model improves on Llama-2-7B across five languages against three objective benchmarks and an LLM-as-a-judge benchmark. |
Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives (2024.acl-long)
Copied to clipboard
| Challenge: | Recent research indicates without external feedback, LLM’s intrinsic reflection is unstable. |
| Approach: | They propose a method that combines self-evaluated and external feedback to improve LLM's reflection. |
| Outcome: | The proposed method improves the quality of self-evaluated feedback and can catalyze more accurate and stable reflection. |
Struct-XLM: A Structure Discovery Multilingual Language Model for Enhancing Cross-lingual Transfer through Reinforcement Learning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods require syntactic labels that are difficult to obtain and of poor quality for low-resource languages. |
| Approach: | They propose a syntactic alignment model that leverages reinforcement learning to discover universal syntaktic structures for cross-lingual PLM alignment. |
| Outcome: | The proposed model improves cross-lingual representation alignment on the XTREME benchmark. |
AutoTaskEval: Towards Domain-Specific and Fine-Grained Evaluation for LLMs (2026.acl-long)
Copied to clipboard
Qingqing Lyu, Linjuan Wu, Yongliang Shen, Hengwei Liu, Hao Li, Shengpei Jiang, Yin Zhang, Weiming Lu
| Challenge: | Existing automated approaches operate within fixed task schemas and often fail to autonomously discover new evaluation dimensions. |
| Approach: | They propose an automated framework that constructs domain-specific benchmarks directly from unstructured corpora using Bloom’s Taxonomy. |
| Outcome: | The proposed framework uncovers a broader and more fine-grained task space than expert-curated benchmarks while producing high-quality instances that preserve established model-level evaluation trends. |
TimeToM: Temporal Space is the Key to Unlocking the Door of Large Language Models’ Theory-of-Mind (2024.findings-acl)
Copied to clipboard
| Challenge: | Theory of Mind (ToM) is the foundation of social interaction and is crucial for social interaction. |
| Approach: | They propose a tool-belief solver that can transform a character’s higher-order beliefs into another character’ s first-order belief under belief communication period. |
| Outcome: | The proposed model improves the ToM capabilities of Large Language Models (LLMs) in multiple scenarios. |