6 papers
Break Through the Compression Bottleneck: From Theory to Practice
Xiusheng Huang, Lu Wang, Yequan Wang +2
As the parameter size of language models continues to grow, effective model compression is required to reduce their computational and memory overhead. Existing compression methods…
Theory-optimal Quantization Based on Flatness
Xiusheng Huang, Zhe Li, Xuanwu Yin +5
Post-training quantization has emerged as a widely adopted technique for compressing and accelerating the inference of Large Language Models (LLMs). The primary challenges in LLMs…
Mutual Enhancement Between Global Tokens and Patch Tokens: From Theory to Practice
Xiusheng Huang, Xin Jiang, Jun Zhao +2
Accurate and effective discrete image tokenization is crucial for long image sequence processing. However, current methods rigidly compress all content at a fixed rate, ignoring th…
R3A: Reinforced Reasoning for Relevance Assessment for RAG in User-Generated Content Platforms
Xiaowei Yuan, Lei Jin, Haoxin Zhang +6
Retrieval-augmented generation (RAG) plays a critical role in user-generated content (UGC) platforms, but its effectiveness critically depends on accurate query-document relevance…
If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs
Siqi Fan, Xiusheng Huang, Yiqun Yao +6
Large language models (LLMs) can carry out human-like dialogue, but unlike humans, they are stateless due to the superposition property. However, during multi-turn, multi-agent int…
Exploiting Contextual Knowledge in LLMs through V-usable Information based Layer Enhancement
Xiaowei Yuan, Zhao Yang, Ziyang Huang +5
Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet they often struggle with context-faithfulness generations that properly reflect context…