6 papers
SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment
Tianyu Dong, Yangyang Liu, Jiang Zhou +9
Sparse Mixture-of-Experts (MoE) architectures have emerged as an increasingly influential paradigm as they offer a strategic balance between parameter scalability and computational…
Do Gender Cues Affect LLM Value Trade-offs? Evidence from a Controlled Decision Benchmark
Yangyang Liu, Dong Yu, Pengyuan Liu
Large language models are increasingly used in value-sensitive decision settings, where irrelevant demographic cues should not alter judgments. We construct the Realistic Value Dec…
From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models
Ling Shi, Xinwei Wu, Xiaohu Zhao +7
While mechanistic interpretability tools like Sparse Autoencoders (SAEs) can uncover meaningful features within Large Language Models (LLMs), a critical gap remains in transforming…
Incentivizing Parametric Knowledge via Reinforcement Learning with Verifiable Rewards for Cross-Cultural Entity Translation
Jiang Zhou, Xiaohu Zhao, Xinwei Wu +8
Cross-cultural entity translation remains challenging for large language models (LLMs) as literal or phonetic renderings are usually yielded instead of culturally appropriate trans…
: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation
Hao Wang, Linlong Xu, Heng Liu +12
Aligning Large Language Models (LLMs) with human preferences is pivotal for Machine Translation (MT), yet current approaches are often hindered by misleading reward signals. Our an…
Marco-LLM: Bridging Languages via Massive Multilingual Training for Cross-Lingual Enhancement
Lingfeng Ming, Bo Zeng, Chenyang Lyu +17
Large Language Models (LLMs) have achieved remarkable progress in recent years; however, their excellent performance is still largely limited to major world languages, primarily En…