A Survey on LLM Inference-Time Self-Improvement

Xiangjue Dong; Maria Teleki; James Caverlee

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arXiv · 2412.14352

A Survey on LLM Inference-Time Self-Improvement

Xiangjue Dong·Maria Teleki·James Caverlee

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Abstract

Techniques that enhance inference through increased computation at test-time have recently gained attention. In this survey, we investigate the current state of LLM Inference-Time Self-Improvement from three different perspectives: Independent Self-improvement, focusing on enhancements via decoding or sampling methods; Context-Aware Self-Improvement, leveraging additional context or datastore; and Model-Aided Self-Improvement, achieving improvement through model collaboration. We provide a comprehensive review of recent relevant studies, contribute an in-depth taxonomy, and discuss challenges and limitations, offering insights for future research.

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Xiangjue Dong, Maria Teleki, James Caverlee. 2024-12-18. A Survey on LLM Inference-Time Self-Improvement. https://arxiv.org/abs/2412.14352

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