Saved in:
| Main Authors: | Zhang, Jianfei, Li, Bei, Bai, Jun, Li, Rumei, Wang, Yanmeng, Lin, Chenghua, Rong, Wenge |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.04579 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment
by: Zhang, Jianfei, et al.
Published: (2024)
by: Zhang, Jianfei, et al.
Published: (2024)
Demonstration Selection for In-Context Learning via Reinforcement Learning
by: Wang, Xubin, et al.
Published: (2024)
by: Wang, Xubin, et al.
Published: (2024)
Linear-Time Demonstration Selection for In-Context Learning via Gradient Estimation
by: Zhang, Ziniu, et al.
Published: (2025)
by: Zhang, Ziniu, et al.
Published: (2025)
Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts
by: Hong, Hanhua, et al.
Published: (2025)
by: Hong, Hanhua, et al.
Published: (2025)
PFME: A Modular Approach for Fine-grained Hallucination Detection and Editing of Large Language Models
by: Deng, Kunquan, et al.
Published: (2024)
by: Deng, Kunquan, et al.
Published: (2024)
ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search
by: Li, Zehan, et al.
Published: (2024)
by: Li, Zehan, et al.
Published: (2024)
On Many-Shot In-Context Learning for Long-Context Evaluation
by: Zou, Kaijian, et al.
Published: (2024)
by: Zou, Kaijian, et al.
Published: (2024)
Many-Shot In-Context Learning
by: Agarwal, Rishabh, et al.
Published: (2024)
by: Agarwal, Rishabh, et al.
Published: (2024)
Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text Ranking
by: Bai, Jun, et al.
Published: (2024)
by: Bai, Jun, et al.
Published: (2024)
Efficient Many-Shot In-Context Learning with Dynamic Block-Sparse Attention
by: Xiao, Emily, et al.
Published: (2025)
by: Xiao, Emily, et al.
Published: (2025)
In-Context Learning Demonstration Selection via Influence Analysis
by: S., Vinay M., et al.
Published: (2024)
by: S., Vinay M., et al.
Published: (2024)
Distilling Many-Shot In-Context Learning into a Cheat Sheet
by: Honda, Ukyo, et al.
Published: (2025)
by: Honda, Ukyo, et al.
Published: (2025)
Enhancing LLMs via High-Knowledge Data Selection
by: Duan, Feiyu, et al.
Published: (2025)
by: Duan, Feiyu, et al.
Published: (2025)
In-Context Learning with Iterative Demonstration Selection
by: Qin, Chengwei, et al.
Published: (2023)
by: Qin, Chengwei, et al.
Published: (2023)
Revisiting Demonstration Selection Strategies in In-Context Learning
by: Peng, Keqin, et al.
Published: (2024)
by: Peng, Keqin, et al.
Published: (2024)
Many-Shot In-Context Learning for Molecular Inverse Design
by: Moayedpour, Saeed, et al.
Published: (2024)
by: Moayedpour, Saeed, et al.
Published: (2024)
Curriculum Demonstration Selection for In-Context Learning
by: Vu, Duc Anh, et al.
Published: (2024)
by: Vu, Duc Anh, et al.
Published: (2024)
Misconfidence-based Demonstration Selection for LLM In-Context Learning
by: Xu, Shangqing, et al.
Published: (2024)
by: Xu, Shangqing, et al.
Published: (2024)
Comparable Demonstrations are Important in In-Context Learning: A Novel Perspective on Demonstration Selection
by: Fan, Caoyun, et al.
Published: (2023)
by: Fan, Caoyun, et al.
Published: (2023)
Delta-KNN: Improving Demonstration Selection in In-Context Learning for Alzheimer's Disease Detection
by: Li, Chuyuan, et al.
Published: (2025)
by: Li, Chuyuan, et al.
Published: (2025)
Beyond Many-Shot Translation: Scaling In-Context Demonstrations For Low-Resource Machine Translation
by: Salim, Luis Frentzen, et al.
Published: (2026)
by: Salim, Luis Frentzen, et al.
Published: (2026)
Sentinel: Decoding Context Utilization via Attention Probing for Efficient LLM Context Compression
by: Zhang, Yong, et al.
Published: (2025)
by: Zhang, Yong, et al.
Published: (2025)
Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning
by: Liu, Hui, et al.
Published: (2024)
by: Liu, Hui, et al.
Published: (2024)
Learning to Select Visual In-Context Demonstrations
by: Lee, Eugene, et al.
Published: (2026)
by: Lee, Eugene, et al.
Published: (2026)
Many-Shot In-Context Learning in Multimodal Foundation Models
by: Jiang, Yixing, et al.
Published: (2024)
by: Jiang, Yixing, et al.
Published: (2024)
Data Whisperer: Efficient Data Selection for Task-Specific LLM Fine-Tuning via Few-Shot In-Context Learning
by: Wang, Shaobo, et al.
Published: (2025)
by: Wang, Shaobo, et al.
Published: (2025)
Towards Compute-Optimal Many-Shot In-Context Learning
by: Golchin, Shahriar, et al.
Published: (2025)
by: Golchin, Shahriar, et al.
Published: (2025)
An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages
by: Lu, Yinhan, et al.
Published: (2026)
by: Lu, Yinhan, et al.
Published: (2026)
Learning to Adapt to Low-Resource Paraphrase Generation
by: Li, Zhigen, et al.
Published: (2024)
by: Li, Zhigen, et al.
Published: (2024)
Meta-Sel: Efficient Demonstration Selection for In-Context Learning via Supervised Meta-Learning
by: Wang, Xubin, et al.
Published: (2026)
by: Wang, Xubin, et al.
Published: (2026)
Language Model as an Annotator: Unsupervised Context-aware Quality Phrase Generation
by: Zhang, Zhihao, et al.
Published: (2023)
by: Zhang, Zhihao, et al.
Published: (2023)
Easier to Judge than to Find: Predicting In-Context Learning Success for Demonstration Selection
by: Wang, Haochun, et al.
Published: (2026)
by: Wang, Haochun, et al.
Published: (2026)
Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning
by: Zhang, Tianhui, et al.
Published: (2024)
by: Zhang, Tianhui, et al.
Published: (2024)
Scaling Laws for Many-Shot In-Context Learning with Self-Generated Annotations
by: Gu, Zhengyao, et al.
Published: (2025)
by: Gu, Zhengyao, et al.
Published: (2025)
Strategic Demonstration Selection for Improved Fairness in LLM In-Context Learning
by: Hu, Jingyu, et al.
Published: (2024)
by: Hu, Jingyu, et al.
Published: (2024)
CogAtom: From Cognitive Atoms to Olympiad-level Mathematical Reasoning in Large Language Models
by: Chen, Zhuofan, et al.
Published: (2025)
by: Chen, Zhuofan, et al.
Published: (2025)
Learn to Select: Exploring Label Distribution Divergence for In-Context Demonstration Selection in Text Classification
by: Jiang, Ye, et al.
Published: (2025)
by: Jiang, Ye, et al.
Published: (2025)
Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study
by: Song, Mingyang, et al.
Published: (2024)
by: Song, Mingyang, et al.
Published: (2024)
Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning
by: Huang, Brandon, et al.
Published: (2024)
by: Huang, Brandon, et al.
Published: (2024)
In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar Tasks
by: Wang, Dingzirui, et al.
Published: (2024)
by: Wang, Dingzirui, et al.
Published: (2024)
Similar Items
-
Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment
by: Zhang, Jianfei, et al.
Published: (2024) -
Demonstration Selection for In-Context Learning via Reinforcement Learning
by: Wang, Xubin, et al.
Published: (2024) -
Linear-Time Demonstration Selection for In-Context Learning via Gradient Estimation
by: Zhang, Ziniu, et al.
Published: (2025) -
Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts
by: Hong, Hanhua, et al.
Published: (2025) -
PFME: A Modular Approach for Fine-grained Hallucination Detection and Editing of Large Language Models
by: Deng, Kunquan, et al.
Published: (2024)