:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Li, Xuan, Yin, Zhe, Gu, Xiaodong, Shen, Beijun
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence
Online Access:	https://arxiv.org/abs/2505.01273
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Transplant Then Regenerate: A New Paradigm for Text Data Augmentation
by: Wang, Guangzhan, et al.
Published: (2025)

DePrompt: Desensitization and Evaluation of Personal Identifiable Information in Large Language Model Prompts
by: Sun, Xiongtao, et al.
Published: (2024)

Neuron-Guided Interpretation of Code LLMs: Where, Why, and How?
by: Yin, Zhe, et al.
Published: (2025)

Just-In-Time Software Defect Prediction via Bi-modal Change Representation Learning
by: Jiang, Yuze, et al.
Published: (2024)

Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey
by: Li, Haochen, et al.
Published: (2024)

Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering
by: Liu, Hongxuan, et al.
Published: (2024)

PromptBridge: Cross-Model Prompt Transfer for Large Language Models
by: Wang, Yaxuan, et al.
Published: (2025)

ELPO: Ensemble Learning Based Prompt Optimization for Large Language Models
by: Zhang, Qing, et al.
Published: (2025)

Continual Learning Using Only Large Language Model Prompting
by: Qiu, Jiabao, et al.
Published: (2024)

Are Large Language Models Good Prompt Optimizers?
by: Ma, Ruotian, et al.
Published: (2024)

Large Language Model Sourcing: A Survey
by: Pang, Liang, et al.
Published: (2025)

Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models
by: Sun, Jiashuo, et al.
Published: (2023)

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks
by: Mu, Lin, et al.
Published: (2025)

On Prompt-Driven Safeguarding for Large Language Models
by: Zheng, Chujie, et al.
Published: (2024)

Do Influence Functions Work on Large Language Models?
by: Li, Zhe, et al.
Published: (2024)

An Empirical Study on Prompt Compression for Large Language Models
by: Zhang, Zheng, et al.
Published: (2025)

Empowering AI to Generate Better AI Code: Guided Generation of Deep Learning Projects with LLMs
by: Xie, Chen, et al.
Published: (2025)

Prompt and Parameter Co-Optimization for Large Language Models
by: Bo, Xiaohe, et al.
Published: (2025)

Large Language Models Prompting With Episodic Memory
by: Do, Dai, et al.
Published: (2024)

On the Worst Prompt Performance of Large Language Models
by: Cao, Bowen, et al.
Published: (2024)

LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model
by: Zhou, Zhi, et al.
Published: (2024)

Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning
by: Wu, Huiwen, et al.
Published: (2024)

DND: Boosting Large Language Models with Dynamic Nested Depth
by: Chen, Tieyuan, et al.
Published: (2025)

LongCodeZip: Compress Long Context for Code Language Models
by: Shi, Yuling, et al.
Published: (2025)

Diverse Prompts: Illuminating the Prompt Space of Large Language Models with MAP-Elites
by: Santos, Gabriel Machado, et al.
Published: (2025)

Hypothesis Testing Prompting Improves Deductive Reasoning in Large Language Models
by: Li, Yitian, et al.
Published: (2024)

Prompt4Trust: A Reinforcement Learning Prompt Augmentation Framework for Clinically-Aligned Confidence Calibration in Multimodal Large Language Models
by: Kriz, Anita, et al.
Published: (2025)

Linear Chain Transformation: Expanding Optimization Dynamics for Fine-Tuning Large Language Models
by: Wang, Yulong, et al.
Published: (2024)

M-Ped: Multi-Prompt Ensemble Decoding for Large Language Models
by: Guo, Jiaxin, et al.
Published: (2024)

APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts
by: Dong, Honghua, et al.
Published: (2024)

Towards Generalist Prompting for Large Language Models by Mental Models
by: Guan, Haoxiang, et al.
Published: (2024)

Prompt-based Depth Pruning of Large Language Models
by: Wee, Juyun, et al.
Published: (2025)

Prompt-Based Value Steering of Large Language Models
by: Abbo, Giulio Antonio, et al.
Published: (2025)

Metacognitive Prompting Improves Understanding in Large Language Models
by: Wang, Yuqing, et al.
Published: (2023)

Unveiling and Manipulating Prompt Influence in Large Language Models
by: Feng, Zijian, et al.
Published: (2024)

Robust Prompt Optimization for Large Language Models Against Distribution Shifts
by: Li, Moxin, et al.
Published: (2023)

MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization
by: Chen, Yuyan, et al.
Published: (2024)

Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models
by: Li, Mingda, et al.
Published: (2024)

Self-Prompting Large Language Models for Zero-Shot Open-Domain QA
by: Li, Junlong, et al.
Published: (2022)

MiniLLM: On-Policy Distillation of Large Language Models
by: Gu, Yuxian, et al.
Published: (2023)