5 citations · 5 across the 14 of their papers we have counts for
7 papers · 1 filter
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…