Cover Image

One size doesn't fit all: Predicting the Number of Examples for In-Context Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chandra, Manish, Ganguly, Debasis, Ounis, Iadh
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LURE-RAG: Lightweight Utility-driven Reranking for Efficient RAG
by: Chandra, Manish, et al.
Published: (2026)

Global quality improvement programs: One size doesn't fit all
by: George D. Politis, et al.
Published: (2024)

Few-shot Prompting for Pairwise Ranking: An Effective Non-Parametric Retrieval Model
by: Sinhababu, Nilanjan, et al.
Published: (2024)

Schroedinger's Threshold: When the AUC doesn't predict Accuracy
by: Opitz, Juri
Published: (2024)

When does word order matter and when doesn't it?
by: Chen, Xuanda, et al.
Published: (2024)

PLR: Plackett-Luce for Reordering In-Context Learning Examples
by: Batorski, Pawel, et al.
Published: (2026)

AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMs
by: Ghosh, Madhusudan, et al.
Published: (2024)

Triplet-Structured Knowledge Integration for Multi-Turn Medical Reasoning
by: Meng, Zhaohan, et al.
Published: (2025)

One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction
by: Liu, Ben, et al.
Published: (2025)

NICE: To Optimize In-Context Examples or Not?
by: Srivastava, Pragya, et al.
Published: (2024)

GD doesn't make the cut: Three ways that non-differentiability affects neural network training
by: Kumar, Siddharth Krishna
Published: (2024)

RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning
by: Scarlatos, Alexander, et al.
Published: (2023)

OptiSeq: Ordering Examples On-The-Fly for In-Context Learning
by: Bhope, Rahul Atul, et al.
Published: (2025)

Reinforcement Learning for Reasoning in Large Language Models with One Training Example
by: Wang, Yiping, et al.
Published: (2025)

Temporal Fact Conflicts in LLMs: Reproducibility Insights from Unifying DYNAMICQA and MULAN
by: Dey, Ritajit, et al.
Published: (2026)

FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction
by: Meng, Zhaohan, et al.
Published: (2024)

Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks
by: Sarukkai, Vishnu, et al.
Published: (2025)

Toward In-Context Teaching: Adapting Examples to Students' Misconceptions
by: Ross, Alexis, et al.
Published: (2024)

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

Learning and Forgetting Unsafe Examples in Large Language Models
by: Zhao, Jiachen, et al.
Published: (2023)

Alexpaca: Learning Factual Clarification Question Generation Without Examples
by: Toles, Matthew, et al.
Published: (2023)

In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization
by: Zhang, Ruiqi, et al.
Published: (2024)

Reinforcement Learning is all You Need
by: Lian, Yongsheng
Published: (2025)

Over-representation of phonological features in basic vocabulary doesn't replicate when controlling for spatial and phylogenetic effects
by: Blum, Frederic
Published: (2025)

SSCAE -- Semantic, Syntactic, and Context-aware natural language Adversarial Examples generator
by: Asl, Javad Rafiei, et al.
Published: (2024)

On the Role of Context in Reading Time Prediction
by: Opedal, Andreas, et al.
Published: (2024)

Towards Auto-Regressive Next-Token Prediction: In-Context Learning Emerges from Generalization
by: Gong, Zixuan, et al.
Published: (2025)

Learning New Tasks from a Few Examples with Soft-Label Prototypes
by: Singh, Avyav Kumar, et al.
Published: (2022)

The Geometric Price of Discrete Logic: Context-driven Manifold Dynamics of Number Representations
by: Zhang, Long, et al.
Published: (2026)

One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs
by: Li, Yinghui, et al.
Published: (2025)

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning
by: Li, Chengye, et al.
Published: (2025)

Using ML filters to help automated vulnerability repairs: when it helps and when it doesn't
by: Camporese, Maria, et al.
Published: (2025)

Learning Task Representations from In-Context Learning
by: Saglam, Baturay, et al.
Published: (2025)

Can Models Learn Skill Composition from Examples?
by: Zhao, Haoyu, et al.
Published: (2024)

Neural Sequence-to-Sequence Modeling with Attention by Leveraging Deep Learning Architectures for Enhanced Contextual Understanding in Abstractive Text Summarization
by: Challagundla, Bhavith Chandra, et al.
Published: (2024)

Designing Informative Metrics for Few-Shot Example Selection
by: Adiga, Rishabh, et al.
Published: (2024)

"In-Context Learning" or: How I learned to stop worrying and love "Applied Information Retrieval"
by: Parry, Andrew, et al.
Published: (2024)

COLD-Steer: Steering Large Language Models via In-Context One-step Learning Dynamics
by: Sharma, Kartik, et al.
Published: (2026)

Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification
by: Liu, Shang, et al.
Published: (2024)

Stronger Random Baselines for In-Context Learning
by: Yauney, Gregory, et al.
Published: (2024)