Cover Image

More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Xiaoqing, Lv, Ang, Liu, Yuhan, Sung, Flood, Liu, Wei, Luan, Jian, Shang, Shuo, Chen, Xiuying, Yan, Rui
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Thinking Before Running! Efficient Code Generation with Thorough Exploration and Optimal Refinement
by: Zhang, Xiaoqing, et al.
Published: (2024)

Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games
by: Zhang, Xiaoqing, et al.
Published: (2025)

HoPE: A Novel Positional Encoding Without Long-Term Decay for Enhanced Context Awareness and Extrapolation
by: Chen, Yuhan, et al.
Published: (2024)

SAGraph: A Large-Scale Social Graph Dataset with Comprehensive Context for Influencer Selection in Marketing
by: Zhang, Xiaoqing, et al.
Published: (2024)

The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News
by: Liu, Yuhan, et al.
Published: (2025)

Weaving Context Across Images: Improving Vision-Language Models through Focus-Centric Visual Chains
by: Zhang, Juntian, et al.
Published: (2025)

Mixture of In-Context Experts Enhance LLMs' Long Context Awareness
by: Lin, Hongzhan, et al.
Published: (2024)

The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents
by: Liu, Yuhan, et al.
Published: (2024)

From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News
by: Liu, Yuhan, et al.
Published: (2024)

A Large-scale Time-aware Agents Simulation for Influencer Selection in Digital Advertising Campaigns
by: Zhang, Xiaoqing, et al.
Published: (2024)

Batch-ICL: Effective, Efficient, and Order-Agnostic In-Context Learning
by: Zhang, Kaiyi, et al.
Published: (2024)

More sophisticated is not always better: comparison of similarity measures for unsupervised learning of pathways in biomolecular simulations
by: Jäger, Miriam, et al.
Published: (2025)

DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning
by: Zhang, Kaichuang, et al.
Published: (2025)

Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use
by: Chen, Yuhan, et al.
Published: (2023)

Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents
by: Deng, Shihan, et al.
Published: (2024)

Many-Shot In-Context Learning
by: Agarwal, Rishabh, et al.
Published: (2024)

MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding
by: Wu, Qinzhuo, et al.
Published: (2024)

Compressing Many-Shots in In-Context Learning
by: Khatri, Devvrit, et al.
Published: (2025)

DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy
by: Sun, Hongda, et al.
Published: (2023)

On Many-Shot In-Context Learning for Long-Context Evaluation
by: Zou, Kaijian, et al.
Published: (2024)

Iterate to Differentiate: Enhancing Discriminability and Reliability in Zero-Shot TTS Evaluation
by: Shen, Shengfan, et al.
Published: (2026)

Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization
by: Lin, Xi, et al.
Published: (2024)

Pastiche Novel Generation Creating: Fan Fiction You Love in Your Favorite Author's Style
by: Han, Xueran, et al.
Published: (2025)

Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey
by: Song, Zirui, et al.
Published: (2025)

Interpreting Key Mechanisms of Factual Recall in Transformer-Based Language Models
by: Lv, Ang, et al.
Published: (2024)

Hotter is not (always) better: Embracing unimodal scaling of biological rates with temperature
by: Sean T. Michaletz, et al.
Published: (2024)

Enhancing Dense Retrievers' Robustness with Group-level Reweighting
by: Han, Peixuan, et al.
Published: (2023)

Scaling Laws for Many-Shot In-Context Learning with Self-Generated Annotations
by: Gu, Zhengyao, et al.
Published: (2025)

More Expressive Attention with Negative Weights
by: Lv, Ang, et al.
Published: (2024)

Deep models for stroke segmentation: do complex architectures always perform better?
by: Zafari-Ghadim, Yalda, et al.
Published: (2024)

Many-Shot In-Context Learning in Multimodal Foundation Models
by: Jiang, Yixing, et al.
Published: (2024)

Many-Shot In-Context Learning for Molecular Inverse Design
by: Moayedpour, Saeed, et al.
Published: (2024)

Towards Compute-Optimal Many-Shot In-Context Learning
by: Golchin, Shahriar, et al.
Published: (2025)

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels
by: Birrell, Jeremiah, et al.
Published: (2025)

Demystifying Diffusion Objectives: Reweighted Losses are Better Variational Bounds
by: Shi, Jiaxin, et al.
Published: (2025)

TapWeight: Reweighting Pretraining Objectives for Task-Adaptive Pretraining
by: Zhang, Ruiyi, et al.
Published: (2024)

MIR-Bench: Can Your LLM Recognize Complicated Patterns via Many-Shot In-Context Reasoning?
by: Yan, Kai, et al.
Published: (2025)

STEP: Success-Rate-Aware Trajectory-Efficient Policy Optimization
by: Chen, Yuhan, et al.
Published: (2025)

Entropy Reweighted Conformal Classification
by: Luo, Rui, et al.
Published: (2024)

Towards More Economical Context-Augmented LLM Generation by Reusing Stored KV Cache
by: Li, Hanchen, et al.
Published: (2025)