A test-time zeroth-order optimization of prompt embeddings using a bounded self-supervised proxy from demonstration log-probabilities improves ICL accuracy and correlates with gains across tasks.
What Makes Good In-Context Examples for
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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.
LC-ICL improves few-shot NER and RE by using label-guided contrastive demonstrations that pair positive samples with error-annotated negative samples.
MLP activations measured as massive activations or first four moments correlate weakly (max |Spearman| = 0.33) with in-context example quality across Llama-3.2-3B, Qwen2.5-3B, and multiple classification/generative tasks, so activation-based active learning should not be used for ICL.
CRAFT is a Pareto-front prompt optimizer that allocates scarce LLM validation calls to candidates near the current front using accuracy- and cost-oriented generators plus NSGA-II retention.
Many-shot CoT-ICL improves when demonstrations are ordered for smooth conceptual progression, with CDS delivering up to 5.42 percentage-point gains on math tasks using 64 examples.
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.
Presents a four-module LLM framework for text-to-SQL on the ALeRCE astro database, evaluated on 110 NL/SQL pairs across 13 models with perfect-match metrics.
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