12 papers
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…
SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution
Xiachong Feng, Yi Jiang, Xiaocheng Feng +9
Social intelligence, the ability to navigate complex interpersonal interactions, presents a fundamental challenge for language agents. Training such agents via reinforcement learni…
Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play
Xiachong Feng, Deyi Yin, Xiaocheng Feng +9
Games offer a compelling paradigm for developing general reasoning capabilities in language models, as they naturally demand strategic planning, probabilistic inference, and adapti…
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,…
LangGPS: Language Separability Guided Data Pre-Selection for Joint Multilingual Instruction Tuning
Yangfan Ye, Xiaocheng Feng, Xiachong Feng +7
Joint multilingual instruction tuning is a widely adopted approach to improve the multilingual instruction-following ability and downstream performance of large language models (LL…
CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning
Yangfan Ye, Xiaocheng Feng, Zekun Yuan +11
Current large language models (LLMs) often exhibit imbalanced multilingual capabilities due to their English-centric training corpora. To address this, existing fine-tuning approac…
Enhancing Non-English Capabilities of English-Centric Large Language Models through Deep Supervision Fine-Tuning
Wenshuai Huo, Xiaocheng Feng, Yichong Huang +9
Large language models (LLMs) have demonstrated significant progress in multilingual language understanding and generation. However, due to the imbalance in training data, their cap…
Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization
Lei Huang, Xiaocheng Feng, Weitao Ma +9
Ensuring contextual faithfulness in retrieval-augmented large language models (LLMs) is crucial for building trustworthy information-seeking systems, particularly in long-form ques…
Exploring Cross-lingual Latent Transplantation: Mutual Opportunities and Open Challenges
Yangfan Ye, Xiaocheng Feng, Xiachong Feng +11
Current large language models (LLMs) often exhibit imbalances in multilingual capabilities and cultural adaptability, largely attributed to their English-centric pre-training data.…
GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization
Yangfan Ye, Xiachong Feng, Xiaocheng Feng +6
News summarization in today's global scene can be daunting with its flood of multilingual content and varied viewpoints from different sources. However, current studies often negle…
Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis
Weitao Ma, Xiaocheng Feng, Weihong Zhong +4
Large language model unlearning has garnered increasing attention due to its potential to address security and privacy concerns, leading to extensive research in the field. However…