2 citations · 2 across the 8 of their papers we have counts for
4 papers · 1 filter
When Personality Meets Quantization: A Layer-wise MBTI Analysis of Quantized LLMs
Yao Fu, Lijia Huang, Xiaomin Li +3
Personality is increasingly important in large language models (LLMs), as it shapes users' trust, engagement, and emotional experiences. While the Myers--Briggs Type Indicator (MBT…
Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation
Yuxuan Jiang, Runchao Li, Shubhashis Roy Dipta +2
While recent work in Reinforcement Learning with Verifiable Rewards (RLVR) has shown that a small subset of critical tokens disproportionately drives reasoning gains, an analogous…
FAEDKV: Infinite-Window Fourier Transform for Unbiased KV Cache Compression
Runchao Li, Yao Fu, Mu Sheng +3
The efficacy of Large Language Models (LLMs) in long-context tasks is often hampered by the substantial memory footprint and computational demands of the Key-Value (KV) cache. Curr…
Dynamic Self-Distillation via Previous Mini-batches for Fine-tuning Small Language Models
Yao Fu, Yin Yu, Xiaotian Han +4
Knowledge distillation (KD) has become a widely adopted approach for compressing large language models (LLMs) to reduce computational costs and memory footprints. However, the avai…