Many-shot CoT-ICL functions as test-time learning when demonstrations are ordered for smooth conceptual progression rather than similarity, enabling a new selection method that improves reasoning performance.
What Makes Good In-Context Examples for GPT -3?
6 Pith papers cite this work. Polarity classification is still indexing.
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Legal2LogicICL improves accuracy and generalization when mapping legal cases to logical formulas by retrieving balanced diverse exemplars at semantic and structural levels, backed by the new Legal2Proleg dataset.
METIS internalizes curriculum judgment in LLM reinforcement fine-tuning by predicting within-prompt reward variance via in-context learning and jointly optimizing with a self-judgment reward, yielding superior performance and up to 67% faster convergence across math, code, and agent benchmarks.
SPARK improves LLM-based test code fault localization by retrieving similar past faults and selectively annotating suspicious lines in new failing tests.
GRaSp optimizes in-context examples for LLMs via synthetic generation, clustering, dimensionality reduction, and genetic algorithms with diversity-adaptive mutation, reaching 45.84% micro-F1 on financial NER with real data and outperforming zero-shot and random few-shot baselines.
LLMs disperse meaning-preserving prompts internally instead of clustering them, which produces an excessively high upper bound on output log-probability differences via Taylor expansion and Cauchy-Schwarz.
citing papers explorer
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Many-Shot CoT-ICL: Making In-Context Learning Truly Learn
Many-shot CoT-ICL functions as test-time learning when demonstrations are ordered for smooth conceptual progression rather than similarity, enabling a new selection method that improves reasoning performance.
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Legal2LogicICL: Improving Generalization in Transforming Legal Cases to Logical Formulas via Diverse Few-Shot Learning
Legal2LogicICL improves accuracy and generalization when mapping legal cases to logical formulas by retrieving balanced diverse exemplars at semantic and structural levels, backed by the new Legal2Proleg dataset.
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Internalizing Curriculum Judgment for LLM Reinforcement Fine-Tuning
METIS internalizes curriculum judgment in LLM reinforcement fine-tuning by predicting within-prompt reward variance via in-context learning and jointly optimizing with a self-judgment reward, yielding superior performance and up to 67% faster convergence across math, code, and agent benchmarks.
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Similar Pattern Annotation via Retrieval Knowledge for LLM-Based Test Code Fault Localization
SPARK improves LLM-based test code fault localization by retrieving similar past faults and selectively annotating suspicious lines in new failing tests.
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GRaSp: Automatic Example Optimization for In-Context Learning in Low-Data Tasks
GRaSp optimizes in-context examples for LLMs via synthetic generation, clustering, dimensionality reduction, and genetic algorithms with diversity-adaptive mutation, reaching 45.84% micro-F1 on financial NER with real data and outperforming zero-shot and random few-shot baselines.
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Understanding the Prompt Sensitivity
LLMs disperse meaning-preserving prompts internally instead of clustering them, which produces an excessively high upper bound on output log-probability differences via Taylor expansion and Cauchy-Schwarz.