2.1k citations · 2.1k across the 25 of their papers we have counts for
25 papers
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation
Shu-Hao Zhang, Le-Tong Huang, Xiang-Sheng Deng +5
Quantization has emerged as a mainstream approach for deploying Large Language Models (LLMs) on resource-constrained devices, yet compressing precision below 4-bit typically causes…
CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering
Qiming Li, Zekai Ye, Xiaocheng Feng +9
Although Large Vision-Language Models (LVLMs) have demonstrated remarkable performance on downstream tasks, they frequently produce contents that deviate from visual information, l…
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,…
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…
Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management
Weitao Ma, Xiaocheng Feng, Lei Huang +7
Effective memory management is essential for large language model agents to navigate long-horizon tasks. Recent research has explored using Reinforcement Learning to develop specia…
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…
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…
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…
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…
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…
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…
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…
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…
Length Controlled Generation for Black-box LLMs
Yuxuan Gu, Wenjie Wang, Xiaocheng Feng +5
Large language models (LLMs) have demonstrated impressive instruction following capabilities, while still struggling to accurately manage the length of the generated text, which is…
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.…
Discrete Modeling via Boundary Conditional Diffusion Processes
Yuxuan Gu, Xiaocheng Feng, Lei Huang +5
We present an novel framework for efficiently and effectively extending the powerful continuous diffusion processes to discrete modeling. Previous approaches have suffered from the…
Advancing Large Language Model Attribution through Self-Improving
Lei Huang, Xiaocheng Feng, Weitao Ma +7
Teaching large language models (LLMs) to generate text with citations to evidence sources can mitigate hallucinations and enhance verifiability in information-seeking systems. Howe…
Learning Fine-Grained Grounded Citations for Attributed Large Language Models
Lei Huang, Xiaocheng Feng, Weitao Ma +8
Despite the impressive performance on information-seeking tasks, large language models (LLMs) still struggle with hallucinations. Attributed LLMs, which augment generated text with…
Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models
Weihong Zhong, Xiaocheng Feng, Liang Zhao +6
Though advanced in understanding visual information with human languages, Large Vision-Language Models (LVLMs) still suffer from multimodal hallucinations. A natural concern is tha…
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…
Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications
Zhangyin Feng, Weitao Ma, Weijiang Yu +7
Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limi…