2.1k citations · 2.1k across the 63 of their papers we have counts for
54 papers · 1 filter
ACTD: Anchor-Based Cross-Tokenizer Distillation with Residual Regularization
Huiyi Zhang, Zijian Li, Xiaocheng Feng +4
Knowledge distillation effectively transfers reasoning capabilities from large language models to lightweight student models. To enable knowledge transfer across disparate model fa…
CultureForest: Understanding and Evaluating Cultural Norm Grounded Reasoning in LLMs
Yangfan Ye, Xiaocheng Feng, Jialong Tang +5
Existing research largely reduces cultural intelligence in LLMs to a knowledge-level problem, overlooking whether models can effectively utilize their acquired knowledge in realist…
Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation
Zekun Yuan, Yangfan Ye, Xiaocheng Feng +5
Large language models (LLMs) have achieved strong performance in general machine translation, yet their ability in culture-aware scenarios remains poorly understood. To bridge this…
x1: Learning to Think Adaptively Across Languages and Cultures
Yangfan Ye, Xiaocheng Feng, Xiachong Feng +8
Languages encode distinct abstractions and inductive priors, yet most large language models (LLMs) overlook this diversity by reasoning in a single dominant language. In this work,…
Can Large Language Models Simulate Human Cognition Beyond Behavioral Imitation?
Yuxuan Gu, Lunjun Liu, Xiaocheng Feng +4
An essential problem in artificial intelligence is whether LLMs can simulate human cognition or merely imitate surface-level behaviors, while existing datasets suffer from either s…
Bootstrapping Exploration with Group-Level Natural Language Feedback in Reinforcement Learning
Lei Huang, Xiang Cheng, Chenxiao Zhao +6
Large language models (LLMs) typically receive diverse natural language (NL) feedback through interaction with the environment. However, current reinforcement learning (RL) algorit…