5 citations · 5 across the 14 of their papers we have counts for
14 papers
LIMIT: Less Is More for Instruction Tuning in Text-to-SQL
Haoyuan Ma, Hengwei Liu, Linjuan Wu +2
Large language models have achieved remarkable progress on Text-to-SQL through reasoning-enhanced fine-tuning, yet existing approaches predominantly rely on massive instruction cor…
When Metrics Reward the Worst Translations: Internalizing Cultural Reasoning for Social Media Translation Evaluation
Yiwen Qiu, Linjuan Wu, Dingming Li +7
Automatic translation quality metrics trained on general-domain corpora systematically fail on social media content, where communicative intent is encoded in culturally loaded expr…
Beyond Rubrics: Exploration-Guided Evaluation Skills for Reward Modeling
Xing Yue, Linjuan Wu, Daoxin Zhang +2
Open-ended reward modeling requires judges that can follow subtle, domain-specific preferences when verifiable answers are unavailable. Existing rubric-based methods often address…
Beyond Literal Translation: Evaluating Cultural Effectiveness in Social Media UGC
Linjuan Wu, Ruiqi Zhang, Xinze Lyu +7
Social media platforms enable large-scale cross-lingual communication, but translating user-generated content (UGC) remains challenging due to its informal style, cultural referenc…
A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM Integration into Upcycled MoE
Hao Zhou, Tianhao Li, Zhijun Wang +6
Expanding Large Language Models~(LLMs) to new languages is a costly endeavor, demanding extensive Continued Pre-Training~(CPT) and data-intensive alignment. While recent data-free…
Language as a Latent Variable for Reasoning Optimization
Linjuan Wu, Haoran Wei, Jialong Tang +4
As LLMs reduce English-centric bias, a surprising trend emerges: non-English responses sometimes outperform English on reasoning tasks. We hypothesize that language functions as a…
Pause or Fabricate? Training Language Models for Grounded Reasoning
Yiwen Qiu, Linjuan Wu, Yizhou Liu +9
Large language models have achieved remarkable progress on complex reasoning tasks. However, they often implicitly fabricate information when inputs are incomplete, producing confi…
LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy Optimization
Xingyu Wu, Yuchen Yan, Shangke Lyu +7
Large reasoning models have achieved remarkable performance through extended chain-of-thought sequences, yet this computational freedom leads to excessive token generation even for…
Hierarchical Budget Policy Optimization for Adaptive Reasoning
Shangke Lyu, Linjuan Wu, Yuchen Yan +7
Large reasoning models achieve remarkable performance through extensive chain-of-thought generation, yet they suffer from a critical inefficiency: applying uniformly extensive reas…
SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation
Siqi Chen, Xinyu Dong, Haolei Xu +10
Large Language Models (LLMs) and Multimodal LLMs have shown promising capabilities for SVG processing, yet existing benchmarks suffer from limited real-world coverage, lack of comp…
Enhancing LLM Language Adaption through Cross-lingual In-Context Pre-training
Linjuan Wu, Haoran Wei, Huan Lin +4
Large language models (LLMs) exhibit remarkable multilingual capabilities despite English-dominated pre-training, attributed to cross-lingual mechanisms during pre-training. Existi…
AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification
Xuan Zhang, Yongliang Shen, Zhe Zheng +6
Large language models (LLMs) have demonstrated remarkable capabilities in tool learning. In real-world scenarios, user queries are often ambiguous and incomplete, requiring effecti…
TimeToM: Temporal Space is the Key to Unlocking the Door of Large Language Models' Theory-of-Mind
Guiyang Hou, Wenqi Zhang, Yongliang Shen +2
Theory of Mind (ToM)-the cognitive ability to reason about mental states of ourselves and others, is the foundation of social interaction. Although ToM comes naturally to humans, i…
Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives
Wenqi Zhang, Yongliang Shen, Linjuan Wu +4
The reflection capacity of Large Language Model (LLM) has garnered extensive attention. A post-hoc prompting strategy, e.g., reflexion and self-refine, refines LLM's response based…