3 citations · 8 across the 9 of their papers we have counts for
9 papers
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…
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…
Extending Context Window of Large Language Models from a Distributional Perspective
Yingsheng Wu, Yuxuan Gu, Xiaocheng Feng +5
Scaling the rotary position embedding (RoPE) has become a common method for extending the context window of RoPE-based large language models (LLMs). However, existing scaling metho…
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information
Yuxin Wang, Minghua Ma, Zekun Wang +7
The colossal parameters and computational overhead of Large Language Models (LLMs) challenge their real-world applications. Network pruning, which targets unstructured or structure…
MoGU: A Framework for Enhancing Safety of Open-Sourced LLMs While Preserving Their Usability
Yanrui Du, Sendong Zhao, Danyang Zhao +6
Large Language Models (LLMs) are increasingly deployed in various applications. As their usage grows, concerns regarding their safety are rising, especially in maintaining harmless…
Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance
Kai Xiong, Xiao Ding, Ting Liu +5
Large language models (LLMs) have developed impressive performance and strong explainability across various reasoning scenarios, marking a significant stride towards mimicking huma…
How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers
Xin Lu, Yanyan Zhao, Bing Qin +3
Pre-trained language models have been proven to possess strong base capabilities, which not only excel in in-distribution language modeling but also show powerful abilities in out-…
Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding
Liang Zhao, Xiachong Feng, Xiaocheng Feng +6
Built upon the Transformer, large language models (LLMs) have captured worldwide attention due to their remarkable abilities. Nevertheless, all Transformer-based models including L…
SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models
Zekun Wang, Jingchang Chen, Wangchunshu Zhou +7
Despite achieving remarkable performance on various vision-language tasks, Transformer-based Vision-Language Models (VLMs) suffer from redundancy in inputs and parameters, signific…