:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Xia, Mingxuan, Wang, Haobo, Li, Yixuan, Yu, Zewei, Wang, Jindong, Zhao, Junbo, Wu, Runze
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Computation and Language
Online Access:	https://arxiv.org/abs/2506.03857
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts
by: Wang, Xinyi, et al.
Published: (2025)

Steer LLM Latents for Hallucination Detection
by: Park, Seongheon, et al.
Published: (2025)

Towards Robust Incremental Learning under Ambiguous Supervision
by: Wang, Rui, et al.
Published: (2025)

Strong Teacher Not Needed? On Distillation in LLM Pretraining
by: Lu, Taiming, et al.
Published: (2026)

LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
by: Pan, Zhuoshi, et al.
Published: (2024)

PromptBench: A Unified Library for Evaluation of Large Language Models
by: Zhu, Kaijie, et al.
Published: (2023)

Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference
by: Adamska, Marta, et al.
Published: (2025)

Anti-Self-Distillation for Reasoning RL via Pointwise Mutual Information
by: Shen, Guobin, et al.
Published: (2026)

Rebellious Student: Reversing Teacher Signals for Reasoning Exploration with Self-Distilled RLVR
by: Kim, Jeonghye, et al.
Published: (2026)

HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation
by: Deng, Zewei, et al.
Published: (2026)

DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AI
by: Liang, Hao, et al.
Published: (2025)

UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
by: Jin, Yiqiao, et al.
Published: (2026)

Energy-based Automated Model Evaluation
by: Peng, Ru, et al.
Published: (2024)

How Can Large Language Models Understand Spatial-Temporal Data?
by: Liu, Lei, et al.
Published: (2024)

Knowledge Distillation in Automated Annotation: Supervised Text Classification with LLM-Generated Training Labels
by: Pangakis, Nicholas, et al.
Published: (2024)

Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation
by: Zhang, Hengyuan, et al.
Published: (2025)

PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning
by: Kim, Gyeongman, et al.
Published: (2024)

BitNet Distillation
by: Wu, Xun, et al.
Published: (2025)

Understanding and Mitigating Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks
by: Li, Miaomiao, et al.
Published: (2025)

From Parameters to Data: A Task-Parameter-Guided Fine-Tuning Pipeline for Efficient LLM Alignment
by: Chen, Hao, et al.
Published: (2026)

CYCLE-INSTRUCT: Fully Seed-Free Instruction Tuning via Dual Self-Training and Cycle Consistency
by: Shen, Zhanming, et al.
Published: (2025)

PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
by: Zhu, Kaijie, et al.
Published: (2023)

SparseEval: Efficient Evaluation of Large Language Models by Sparse Optimization
by: Zhang, Taolin, et al.
Published: (2026)

CultureLLM: Incorporating Cultural Differences into Large Language Models
by: Li, Cheng, et al.
Published: (2024)

Merge-of-Thought Distillation
by: Shen, Zhanming, et al.
Published: (2025)

From Variance to Invariance: Qualitative Content Analysis for Narrative Graph Annotation
by: Huang, Junbo, et al.
Published: (2026)

Automatic Prompt Optimization with Prompt Distillation
by: Dyagin, Ernest A., et al.
Published: (2025)

YODA: Teacher-Student Progressive Learning for Language Models
by: Lu, Jianqiao, et al.
Published: (2024)

Optimal Brain Iterative Merging: Mitigating Interference in LLM Merging
by: Wang, Zhixiang, et al.
Published: (2025)

Reinforcement Learning-based Knowledge Distillation with LLM-as-a-Judge
by: Shen, Yiyang, et al.
Published: (2026)

In Good GRACEs: Principled Teacher Selection for Knowledge Distillation
by: Panigrahi, Abhishek, et al.
Published: (2025)

Harnessing Negative Signals: Reinforcement Distillation from Teacher Data for LLM Reasoning
by: Xu, Shuyao, et al.
Published: (2025)

On Teacher Hacking in Language Model Distillation
by: Tiapkin, Daniil, et al.
Published: (2025)

Do Students Debias Like Teachers? On the Distillability of Bias Mitigation Methods
by: Cheng, Jiali, et al.
Published: (2025)

DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback
by: Khan, Zaid, et al.
Published: (2024)

LLM-NEO: Parameter Efficient Knowledge Distillation for Large Language Models
by: Yang, Runming, et al.
Published: (2024)

Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs
by: Hua, Andong, et al.
Published: (2025)

Data Contamination Calibration for Black-box LLMs
by: Ye, Wentao, et al.
Published: (2024)

LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization
by: Wu, Yuanchen, et al.
Published: (2025)

Towards Cross-Table Masked Pretraining for Web Data Mining
by: Ye, Chao, et al.
Published: (2023)