:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Li, Huayang, Zhao, Tianyu, Cai, Deng, Sproat, Richard
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Computation and Language
Online Access:	https://arxiv.org/abs/2512.14391
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SeqPE: Transformer with Sequential Position Encoding
by: Li, Huayang, et al.
Published: (2025)

PoPE: Legendre Orthogonal Polynomials Based Position Encoding for Large Language Models
by: Aggarwal, Arpit
Published: (2024)

ReAGent: A Model-agnostic Feature Attribution Method for Generative Language Models
by: Zhao, Zhixue, et al.
Published: (2024)

ReALM: Reference Resolution As Language Modeling
by: Moniz, Joel Ruben Antony, et al.
Published: (2024)

ReFT: Representation Finetuning for Language Models
by: Wu, Zhengxuan, et al.
Published: (2024)

ReIFE: Re-evaluating Instruction-Following Evaluation
by: Liu, Yixin, et al.
Published: (2024)

ReAct Meets ActRe: When Language Agents Enjoy Training Data Autonomy
by: Yang, Zonghan, et al.
Published: (2024)

ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding
by: Li, Jia-Nan, et al.
Published: (2025)

Self-Speculative Biased Decoding for Faster Re-Translation
by: Zeng, Linxiao, et al.
Published: (2025)

ReLearn: Unlearning via Learning for Large Language Models
by: Xu, Haoming, et al.
Published: (2025)

ReCode: Unify Plan and Action for Universal Granularity Control
by: Yu, Zhaoyang, et al.
Published: (2025)

Softplus Attention with Re-weighting Boosts Length Extrapolation in Large Language Models
by: Gao, Bo, et al.
Published: (2025)

Re-Tuning: Overcoming the Compositionality Limits of Large Language Models with Recursive Tuning
by: Pasewark, Eric, et al.
Published: (2024)

RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents
by: Chen, Weizhe, et al.
Published: (2024)

Large Language Models to Diffusion Finetuning
by: Cetin, Edoardo, et al.
Published: (2025)

RePO: Replay-Enhanced Policy Optimization
by: Li, Siheng, et al.
Published: (2025)

RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogramming
by: Wang, Hao, et al.
Published: (2024)

ReCrit: Transition-Aware Reinforcement Learning for Scientific Critic Reasoning
by: Xu, Wanghan, et al.
Published: (2026)

ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks
by: Yu, Xiaodong, et al.
Published: (2023)

FCoReBench: Can Large Language Models Solve Challenging First-Order Combinatorial Reasoning Problems?
by: Mittal, Chinmay, et al.
Published: (2024)

LoRe: Personalizing LLMs via Low-Rank Reward Modeling
by: Bose, Avinandan, et al.
Published: (2025)

Nested-ReFT: Efficient Reinforcement Learning for Large Language Model Fine-Tuning via Off-Policy Rollouts
by: Heuillet, Maxime, et al.
Published: (2025)

ClinicRealm: Re-evaluating Large Language Models with Conventional Machine Learning for Non-Generative Clinical Prediction Tasks
by: Zhu, Yinghao, et al.
Published: (2024)

Re-evaluating the Need for Multimodal Signals in Unsupervised Grammar Induction
by: Li, Boyi, et al.
Published: (2022)

GeRe: Towards Efficient Anti-Forgetting in Continual Learning of LLM via General Samples Replay
by: Zhang, Yunan, et al.
Published: (2025)

The Role of Diversity in In-Context Learning for Large Language Models
by: Xiao, Wenyang, et al.
Published: (2025)

The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs
by: Janiak, Denis, et al.
Published: (2025)

ReDit: Reward Dithering for Improved LLM Policy Optimization
by: Wei, Chenxing, et al.
Published: (2025)

Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training
by: Yang, Kailai, et al.
Published: (2025)

Large Language Models are Miscalibrated In-Context Learners
by: Li, Chengzu, et al.
Published: (2023)

Revisiting In-Context Learning with Long Context Language Models
by: Baek, Jinheon, et al.
Published: (2024)

Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
by: Zhang, Qizheng, et al.
Published: (2025)

Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization
by: Nakamura, Taishi, et al.
Published: (2025)

SuRe: Surprise-Driven Prioritised Replay for Continual LLM Learning
by: Hazard, Hugo, et al.
Published: (2025)

RIPPLECOT: Amplifying Ripple Effect of Knowledge Editing in Language Models via Chain-of-Thought In-Context Learning
by: Zhao, Zihao, et al.
Published: (2024)

Re-evaluating Automatic LLM System Ranking for Alignment with Human Preference
by: Gao, Mingqi, et al.
Published: (2024)

Re2: A Consistency-ensured Dataset for Full-stage Peer Review and Multi-turn Rebuttal Discussions
by: Zhang, Daoze, et al.
Published: (2025)

Predicting Training Re-evaluation Curves Enables Effective Data Curriculums for LLMs
by: Bergsma, Shane, et al.
Published: (2025)

Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-Training
by: Cunegatti, Elia, et al.
Published: (2024)

Variational Reasoning for Language Models
by: Zhou, Xiangxin, et al.
Published: (2025)