Cover Image

Towards the Effect of Examples on In-Context Learning: A Theoretical Case Study

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	He, Pengfei, Cui, Yingqian, Xu, Han, Liu, Hui, Yamada, Makoto, Tang, Jiliang, Xing, Yue
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Superiority of Multi-Head Attention in In-Context Linear Regression
by: Cui, Yingqian, et al.
Published: (2024)

A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration
by: Cui, Yingqian, et al.
Published: (2024)

DiffusionShield: A Watermark for Copyright Protection against Generative Diffusion Models
by: Cui, Yingqian, et al.
Published: (2023)

Multi-Faceted Studies on Data Poisoning can Advance LLM Development
by: He, Pengfei, et al.
Published: (2025)

Data Poisoning for In-context Learning
by: He, Pengfei, et al.
Published: (2024)

Adaptive Test-Time Reasoning via Reward-Guided Dual-Phase Search
by: Cui, Yingqian, et al.
Published: (2025)

Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysis
by: Lin, Yuping, et al.
Published: (2024)

Crafting Reversible SFT Behaviors in Large Language Models
by: Lin, Yuping, et al.
Published: (2026)

How Do Latent Reasoning Methods Perform Under Weak and Strong Supervision?
by: Cui, Yingqian, et al.
Published: (2026)

A Simple Plug-in for Improving Eviction-Based KV Cache Compression
by: Lin, Yuping, et al.
Published: (2026)

Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models
by: Cui, Yingqian, et al.
Published: (2025)

Make LLMs better zero-shot reasoners: Structure-orientated autonomous reasoning
by: He, Pengfei, et al.
Published: (2024)

SoK: Machine Unlearning for Large Language Models
by: Ren, Jie, et al.
Published: (2025)

Stealthy Backdoor Attack via Confidence-driven Sampling
by: He, Pengfei, et al.
Published: (2023)

Copyright Protection in Generative AI: A Technical Perspective
by: Ren, Jie, et al.
Published: (2024)

Memory Injection Attacks on LLM Agents via Query-Only Interaction
by: Dong, Shen, et al.
Published: (2025)

Theoretical Understanding of In-Context Learning in Shallow Transformers with Unstructured Data
by: Xing, Yue, et al.
Published: (2024)

Difficult Examples Hurt Unsupervised Contrastive Learning: A Theoretical Perspective
by: Zhang, Yi-Ge, et al.
Published: (2025)

Towards a Theoretical Understanding to the Generalization of RLHF
by: Li, Zhaochun, et al.
Published: (2026)

Exploring Memorization in Fine-tuned Language Models
by: Zeng, Shenglai, et al.
Published: (2023)

Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal Transport
by: Zeng, Siqi, et al.
Published: (2024)

Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective
by: Zeng, Shenglai, et al.
Published: (2024)

PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis
by: Ishikawa, Satoki, et al.
Published: (2024)

Impact of Positional Encoding: Clean and Adversarial Rademacher Complexity for Transformers under In-Context Regression
by: He, Weiyi, et al.
Published: (2025)

Many-to-Many Matching via Sparsity Controlled Optimal Transport
by: Liu, Weijie, et al.
Published: (2025)

Can Mamba Learn In Context with Outliers? A Theoretical Generalization Analysis
by: Li, Hongkang, et al.
Published: (2025)

Towards Understanding Link Predictor Generalizability Under Distribution Shifts
by: Revolinsky, Jay, et al.
Published: (2024)

Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion
by: Shomer, Harry, et al.
Published: (2024)

Game-Theoretic Unlearnable Example Generator
by: Liu, Shuang, et al.
Published: (2024)

Understanding the Generalization of In-Context Learning in Transformers: An Empirical Study
by: Zhang, Xingxuan, et al.
Published: (2025)

Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach
by: Han, Haoyu, et al.
Published: (2024)

Learning Structured Representations with Hyperbolic Embeddings
by: Sinha, Aditya, et al.
Published: (2024)

MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples
by: Chen, Tao, et al.
Published: (2023)

Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning
by: Li, Lanqing, et al.
Published: (2024)

An Empirical Study of Self-supervised Learning with Wasserstein Distance
by: Yamada, Makoto, et al.
Published: (2023)

Sharpness-Aware Data Poisoning Attack
by: He, Pengfei, et al.
Published: (2023)

PEAR: Planner-Executor Agent Robustness Benchmark
by: Dong, Shen, et al.
Published: (2025)

FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models
by: Cui, Yingqian, et al.
Published: (2023)

Theoretically Guaranteed Distribution Adaptable Learning
by: Xu, Chao, et al.
Published: (2024)

Frame Theoretical Derivation of Three Factor Learning Rule for Oja's Subspace Rule
by: Yamada, Taiki
Published: (2026)