← all papers · overview

Evolving Llms' Self-refinement Capability Via Synergistic Training-inference Optimization

Abstract

Self-Refinement refers to a model's ability to revise its own responses to produce improved outputs. This capability can also serve as a fundamental mechanism for Self-Improvement, for example, by reconstructing datasets with refined results to enhance intrinsic model performance. However, our comprehensive experiments reveal that large language models (LLMs) show no clear evidence of inherent Sel

Related papers

Ranked by semantic similarity — how closely each paper's abstract matches this one (100% = near-identical topic).