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20232026
most citedA Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

2.1k citations · 2.1k across the 25 of their papers we have counts for

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Showing 2025Show all

8 papers · 1 filter

cs.CL2025

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…

cs.CL2025

Context-Aware Hierarchical Taxonomy Generation for Scientific Papers via LLM-Guided Multi-Aspect Clustering

Kun Zhu, Lizi Liao, Yuxuan Gu +3

The rapid growth of scientific literature demands efficient methods to organize and synthesize research findings. Existing taxonomy construction methods, leveraging unsupervised cl…

cs.CL2025

Language-Specific Layer Matters: Efficient Multilingual Enhancement for Large Vision-Language Models

Yuchun Fan, Yilin Wang, Yongyu Mu +5

Large vision-language models (LVLMs) have demonstrated exceptional capabilities in understanding visual information with human languages but also exhibit an imbalance in multilingu…

cs.CL2025

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…

cs.CL2025

One for All: Update Parameterized Knowledge Across Multiple Models

Weitao Ma, Xiyuan Du, Xiaocheng Feng +8

Large language models (LLMs) encode vast world knowledge but struggle to stay up-to-date, often leading to errors and hallucinations. Knowledge editing offers an efficient alternat…

cs.AI2025

From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems

Zekun Zhou, Xiaocheng Feng, Lei Huang +11

Research is a fundamental process driving the advancement of human civilization, yet it demands substantial time and effort from researchers. In recent years, the rapid development…

cs.CL2025

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…

cs.CL2025★ 1 cited

SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment

Yuchun Fan, Yongyu Mu, Yilin Wang +7

Despite the significant improvements achieved by large language models (LLMs) in English reasoning tasks, these models continue to struggle with multilingual reasoning. Recent stud…