  Large Language Models (LLMs) are powerful models for generation tasks, but
they may not generate good quality outputs in their first attempt. Apart from
model fine-tuning, existing approaches to improve prediction accuracy and
quality typically involve LLM self-improvement / self-reflection that
incorporate feedback from models themselves. Despite their effectiveness, these
methods are hindered by their high computational cost and lack of scalability.
In this work, we propose CERET, a method for refining text generations by
considering semantic stability, entailment and inter-sample uncertainty
measures. Experimental results show that CERET outperforms Self-consistency and
Self-rerank baselines consistently under various task setups, by ~1.6% in
Rouge-1 for abstractive summarization and ~3.5% in hit rate for question
answering. Compared to LLM Self-rerank method, our approach only requires 9.4%
of its latency and is more cost-effective.
