# A Survey on LLM Inference-Time Self-Improvement

**Authors:** Xiangjue Dong, Maria Teleki, James Caverlee  
**arXiv:** [2412.14352](https://arxiv.org/abs/2412.14352)  
**Date:** 2024-12-18  
**Summary page:** https://www.summarizepaper.com/en/paper/2412.14352v1/

## Key points

- Survey on LLM Inference-Time Self-Improvement
- Focus on enhancing inference through increased computation at test-time
- Independent Self-improvement
- Context-Aware Self-Improvement
- Model-Aided Self-Improvement
- Comprehensive overview of recent relevant studies provided
- Discussion on challenges and limitations for future research insights
- Limitation of not presenting all methods with exhaustive technical details due to space constraints highlighted
- Focus on methods from key sources like ACL, EMNLP, NAACL, NeurIPS, ICLR, ICML, and arXiv in recent years
- Ethical considerations raised regarding social bias and economic equity within LLM-related activities

## Summary

In this survey on LLM Inference-Time Self-Improvement, the focus is on techniques that enhance inference through increased computation at test-time. The study explores three main perspectives: Independent Self-improvement, Context-Aware Self-Improvement, and Model-Aided Self-Improvement. The review provides a comprehensive overview of recent relevant studies and discusses challenges and limitations to provide insights for future research. One key aspect highlighted is the limitation of not being able to present all methods with exhaustive technical details due to space constraints. The focus remains on methods from key sources like ACL, EMNLP, NAACL, NeurIPS, ICLR, ICML, and arXiv in recent years. Ethical considerations are also brought up in the survey regarding social bias and economic equity within LLM-related activities.