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Formality is Favored: Unraveling the Learning Preferences of Large Language Models on Data with Conflicting Knowledge
Jiahuan Li, Yiqing Cao, Shujian Huang +1
Having been trained on massive pretraining data, large language models have shown excellent performance on many knowledge-intensive tasks. However, pretraining data tends to contai…
MoE-LPR: Multilingual Extension of Large Language Models through Mixture-of-Experts with Language Priors Routing
Hao Zhou, Zhijun Wang, Shujian Huang +6
Large Language Models (LLMs) are often English-centric due to the disproportionate distribution of languages in their pre-training data. Enhancing non-English language capabilities…
Hallu-PI: Evaluating Hallucination in Multi-modal Large Language Models within Perturbed Inputs
Peng Ding, Jingyu Wu, Jun Kuang +6
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable performance on various visual-language understanding and generation tasks. However, MLLMs occasionally genera…
Multilingual Contrastive Decoding via Language-Agnostic Layers Skipping
Wenhao Zhu, Sizhe Liu, Shujian Huang +3
Decoding by contrasting layers (DoLa), is designed to improve the generation quality of large language models (LLMs) by contrasting the prediction probabilities between an early ex…
PreAlign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual Alignment
Jiahuan Li, Shujian Huang, Aarron Ching +2
Large language models demonstrate reasonable multilingual abilities, despite predominantly English-centric pretraining. However, the spontaneous multilingual alignment in these mod…
Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners
Shimao Zhang, Changjiang Gao, Wenhao Zhu +6
Recently, Large Language Models (LLMs) have shown impressive language capabilities. While most of the existing LLMs have very unbalanced performance across different languages, mul…
Large Language Models are Limited in Out-of-Context Knowledge Reasoning
Peng Hu, Changjiang Gao, Ruiqi Gao +2
Large Language Models (LLMs) possess extensive knowledge and strong capabilities in performing in-context reasoning. However, previous work challenges their out-of-context reasonin…
Why Not Transform Chat Large Language Models to Non-English?
Xiang Geng, Ming Zhu, Jiahuan Li +14
The scarcity of non-English data limits the development of non-English large language models (LLMs). Transforming English-centric LLMs to non-English has been identified as an effe…
The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights
Wenhao Zhu, Shujian Huang, Fei Yuan +3
Bridging the significant gap between large language model's English and non-English performance presents a great challenge. While some previous studies attempt to mitigate this gap…
Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual Knowledge Alignment, But Only Shallowly
Changjiang Gao, Hongda Hu, Peng Hu +3
Despite their strong ability to retrieve knowledge in English, current large language models show imbalance abilities in different languages. Two approaches are proposed to address…
Improved Paraphrase Generation via Controllable Latent Diffusion
Wei Zou, Ziyuan Zhuang, Xiang Geng +3
Paraphrase generation strives to generate high-quality and diverse expressions of a given text, a domain where diffusion models excel. Though SOTA diffusion generation reconciles g…
MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine Translation
Jiahuan Li, Shanbo Cheng, Shujian Huang +1
Large Language Models (LLM) have demonstrated their strong ability in the field of machine translation (MT), yet they suffer from high computational cost and latency. Therefore, tr…
Measuring Meaning Composition in the Human Brain with Composition Scores from Large Language Models
Changjiang Gao, Jixing Li, Jiajun Chen +1
The process of meaning composition, wherein smaller units like morphemes or words combine to form the meaning of phrases and sentences, is essential for human sentence comprehensio…
Question Translation Training for Better Multilingual Reasoning
Wenhao Zhu, Shujian Huang, Fei Yuan +3
Large language models show compelling performance on reasoning tasks but they tend to perform much worse in languages other than English. This is unsurprising given that their trai…
MAPO: Advancing Multilingual Reasoning through Multilingual Alignment-as-Preference Optimization
Shuaijie She, Wei Zou, Shujian Huang +4
Though reasoning abilities are considered language-agnostic, existing LLMs exhibit inconsistent reasoning abilities across different languages, e.g., reasoning in the dominant lang…