Yilin Wang

Why Knowing Both Hops Is Not Enough: Understanding Two-Hop Generalization in Language Models

Zili Zhang, Yilin Wang, Heng Wang, et al.

LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance

Yuchun Fan, Bei Li, Peiguang Li, et al.

DIAL-KG: Schema-Free Incremental Knowledge Graph Construction via Dynamic Schema Induction and Evolution-Intent Assessment

Weidong Bao, Yilin Wang, Ruyu Gao, et al.

DaPT: A Dual-Path Framework for Multilingual Multi-hop Question Answering

Yilin Wang, Yuchun Fan, Jiaoyang Li, et al.

This paper addresses the challenge of answering complex multi-step questions across multiple languages using AI systems. The researchers created new multilingual benchmarks for testing these systems and developed DaPT, a framework that processes questions in both the original language and English simultaneously to better retrieve relevant information and generate answers. Their approach significantly outperforms existing systems, achieving 18.3% improvement on the hardest benchmark.