Imagen de Portada

Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhou, Han, Wan, Xingchen, Proleev, Lev, Mincu, Diana, Chen, Jilin, Heller, Katherine, Roy, Subhrajit
Formato:	Preprint
Publicado:	2023
Materias:	Computation and Language Artificial Intelligence Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Auto-Demo Prompting: Leveraging Generated Outputs as Demonstrations for Enhanced Batch Prompting
por: Feng, Longyu, et al.
Publicado: (2024)

Enhancing In-context Learning via Linear Probe Calibration
por: Abbas, Momin, et al.
Publicado: (2024)

ICPO: Illocution-Calibrated Policy Optimization for Multi-Turn Conversation
por: Wang, Zhebo, et al.
Publicado: (2026)

How Confident Is the First Token? An Uncertainty-Calibrated Prompt Optimization Framework for Large Language Model Classification and Understanding
por: Chen, Wei, et al.
Publicado: (2026)

Agentic Confidence Calibration
por: Zhang, Jiaxin, et al.
Publicado: (2026)

Credence Calibration Game? Calibrating Large Language Models through Structured Play
por: Fang, Ke, et al.
Publicado: (2025)

Double-Calibration: Towards Reliable LLMs via Calibrating Knowledge and Reasoning Confidence
por: Lu, Yuyin, et al.
Publicado: (2026)

A Survey of Calibration Process for Black-Box LLMs
por: Xie, Liangru, et al.
Publicado: (2024)

Asymmetric Advantage Modulation Calibrates Entropy Dynamics in RLVR
por: Gu, Hengrui, et al.
Publicado: (2026)

AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning
por: Zhou, Han, et al.
Publicado: (2023)

Agentic Policy Optimization via Instruction-Policy Co-Evolution
por: Zhou, Han, et al.
Publicado: (2025)

PSC: Extending Context Window of Large Language Models via Phase Shift Calibration
por: Zhu, Wenqiao, et al.
Publicado: (2025)

Full-ECE: A Metric For Token-level Calibration on Large Language Models
por: Liu, Han, et al.
Publicado: (2024)

Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization
por: Wan, Xingchen, et al.
Publicado: (2024)

Rethinking Layer Redundancy: Calibration Matters More Than Search in LLM Depth Pruning
por: Kim, Minkyu, et al.
Publicado: (2026)

Calibration Across Layers: Understanding Calibration Evolution in LLMs
por: Joshi, Abhinav, et al.
Publicado: (2025)

Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization
por: Hsieh, Cheng-Yu, et al.
Publicado: (2024)

A Study on the Calibration of In-context Learning
por: Zhang, Hanlin, et al.
Publicado: (2023)

Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration
por: Lv, Hang, et al.
Publicado: (2026)

Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases
por: Levi, Elad, et al.
Publicado: (2024)

Calibrated Language Models Must Hallucinate
por: Kalai, Adam Tauman, et al.
Publicado: (2023)

Where to show Demos in Your Prompt: A Positional Bias of In-Context Learning
por: Cobbina, Kwesi, et al.
Publicado: (2025)

CAF-Score: Calibrating CLAP with LALMs for Reference-free Audio Captioning Evaluation
por: Lee, Insung, et al.
Publicado: (2026)

On Calibration of Large Language Models: From Response To Capability
por: Yang, Sin-Han, et al.
Publicado: (2026)

SEAL: Steerable Reasoning Calibration of Large Language Models for Free
por: Chen, Runjin, et al.
Publicado: (2025)

Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs
por: Li, Zhuo, et al.
Publicado: (2024)

Atomic Calibration of LLMs in Long-Form Generations
por: Zhang, Caiqi, et al.
Publicado: (2024)

Calibrating Verbalized Probabilities for Large Language Models
por: Wang, Cheng, et al.
Publicado: (2024)

Self-Consistency Boosts Calibration for Math Reasoning
por: Wang, Ante, et al.
Publicado: (2024)

Calibrating Verbalized Confidence with Self-Generated Distractors
por: Wang, Victor, et al.
Publicado: (2025)

Fact-Level Confidence Calibration and Self-Correction
por: Yuan, Yige, et al.
Publicado: (2024)

Calibrating Reasoning in Language Models with Internal Consistency
por: Xie, Zhihui, et al.
Publicado: (2024)

Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies
por: Zhou, Han, et al.
Publicado: (2025)

NoisyICL: A Little Noise in Model Parameters Calibrates In-context Learning
por: Zhao, Yufeng, et al.
Publicado: (2024)

Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards
por: Ma, Zhengzhao, et al.
Publicado: (2026)

Context Engineering 2.0: The Context of Context Engineering
por: Hua, Qishuo, et al.
Publicado: (2025)

CoSpaDi: Compressing LLMs via Calibration-Guided Sparse Dictionary Learning
por: Makhov, Denis, et al.
Publicado: (2025)

Calibrating Language Models with Adaptive Temperature Scaling
por: Xie, Johnathan, et al.
Publicado: (2024)

On the Entropy Calibration of Language Models
por: Cao, Steven, et al.
Publicado: (2025)

Rethinking the Chain-of-Thought: The Roles of In-Context Learning and Pre-trained Priors
por: Yang, Hao, et al.
Publicado: (2025)