55 citations · 375 across the 77 of their papers we have counts for
77 papers
SDGO: Self-Discrimination-Guided Optimization for Consistent Safety in Large Language Models
Peng Ding, Wen Sun, Dailin Li +4
Large Language Models (LLMs) excel at various natural language processing tasks but remain vulnerable to jailbreaking attacks that induce harmful content generation. In this paper,…
Why Not Act on What You Know? Unleashing Safety Potential of LLMs via Self-Aware Guard Enhancement
Peng Ding, Jun Kuang, Zongyu Wang +4
Large Language Models (LLMs) have shown impressive capabilities across various tasks but remain vulnerable to meticulously crafted jailbreak attacks. In this paper, we identify a c…
The First Impression Problem: Internal Bias Triggers Overthinking in Reasoning Models
Renfei Dang, Zhening Li, Shujian Huang +1
Reasoning models often exhibit overthinking, characterized by redundant reasoning steps. We identify \emph{internal bias} elicited by the input question as a key trigger of such be…
Understanding LLMs' Cross-Lingual Context Retrieval: How Good It Is And Where It Comes From
Changjiang Gao, Hankun Lin, Xin Huang +5
Cross-lingual context retrieval (extracting contextual information in one language based on requests in another) is a fundamental aspect of cross-lingual alignment, but the perform…
Generalizing From Short to Long: Effective Data Synthesis for Long-Context Instruction Tuning
Wenhao Zhu, Pinzhen Chen, Hanxu Hu +4
Long-context modelling for large language models (LLMs) has been a key area of recent research because many real world use cases require reasoning over longer inputs such as docume…
Extend Adversarial Policy Against Neural Machine Translation via Unknown Token
Wei Zou, Shujian Huang, Jiajun Chen
Generating adversarial examples contributes to mainstream neural machine translation~(NMT) robustness. However, popular adversarial policies are apt for fixed tokenization, hinderi…
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…
Extroversion or Introversion? Controlling The Personality of Your Large Language Models
Yanquan Chen, Zhen Wu, Junjie Guo +2
Large language models (LLMs) exhibit robust capabilities in text generation and comprehension, mimicking human behavior and exhibiting synthetic personalities. However, some LLMs h…
Large Language Models Are Cross-Lingual Knowledge-Free Reasoners
Peng Hu, Sizhe Liu, Changjiang Gao +5
Large Language Models have demonstrated impressive reasoning capabilities across multiple languages. However, the relationship between capabilities in different languages is less e…
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…
EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling
Shimao Zhang, Yu Bao, Shujian Huang
Recently, Large Language Models (LLMs) have demonstrated outstanding performance across a wide range of downstream language tasks. Temperature sampling is a commonly used decoding…
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…
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…
Cobra Effect in Reference-Free Image Captioning Metrics
Zheng Ma, Changxin Wang, Yawen Ouyang +4
Evaluating the compatibility between textual descriptions and corresponding images represents a core endeavor within multi-modal research. In recent years, a proliferation of refer…
Multi-Candidate Speculative Decoding
Sen Yang, Shujian Huang, Xinyu Dai +1
Large language models have shown impressive capabilities across a variety of NLP tasks, yet their generating text autoregressively is time-consuming. One way to speed them up is sp…
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…