3 citations · 5 across the 12 of their papers we have counts for
7 papers · 1 filter
Robust Reasoning via Dynamic Token Selection for Distribution-Aligned Self-Distillation
Ruiqi Zhang, Lingxiang Wang, Hainan Zhang Zhiming Zheng
Self-distillation improves learning efficiency by rewriting reference answers as training data that better matches the model's own distribution. However, reference answers also int…
From Unfamiliar to Familiar: Detecting Pre-training Data via Gradient Deviations in Large Language Models
Ruiqi Zhang, Lingxiang Wang, Hainan Zhang +2
Pre-training data detection for LLMs is essential for addressing copyright concerns and mitigating benchmark contamination. Existing methods mainly focus on the likelihood-based st…
Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models
Ruiqi Zhang, Changyi Xiao, Yixin Cao
With the rapid advancement of large reasoning models, long Chain-of-Thought (CoT) prompting has demonstrated strong performance on complex tasks. However, this often comes with a s…
How Do LLMs Perform Two-Hop Reasoning in Context?
Tianyu Guo, Hanlin Zhu, Ruiqi Zhang +4
``Socrates is human. All humans are mortal. Therefore, Socrates is mortal.'' This form of argument illustrates a typical pattern of two-hop reasoning. Formally, two-hop reasoning r…
Fast Best-of-N Decoding via Speculative Rejection
Hanshi Sun, Momin Haider, Ruiqi Zhang +6
The safe and effective deployment of Large Language Models (LLMs) involves a critical step called alignment, which ensures that the model's responses are in accordance with human p…
Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning
Chongyu Fan, Jiancheng Liu, Licong Lin +4
This work studies the problem of large language model (LLM) unlearning, aiming to remove unwanted data influences (e.g., copyrighted or harmful content) while preserving model util…