Large Language Models (LLMs) are increasingly being considered for high-stakes decision-making, yet their application in statistical risk analysis remains largely underexplored. A central challenge in this domain is enabling LLMs to effectively leverage historical data. To address this, we propose novel methods for extracting key information from raw data and translating it into structured contextual input within the LLM prompt. Applying our methods to a case study of power outage risk assessment, we demonstrate that this contextualization strategy significantly improves the LLM’s performance in risk assessment tasks. While the LLM’s prediction performance still does not match that of a standard machine learning model, the LLM-based approach offers distinct advantages in versatility and interpretability. These findings demonstrate a new paradigm for contextualizing data to support risk assessment.

Self-reflection for Large LanguageModels (LLMs) has gained significant attention. Existing approaches involve models iterating and improving their previous responses based on LLMs’ internal reflection ability or external feedback. However, recent research has raised doubts about whether intrinsic self-correction without external feedback may even degrade performance. Based on our empirical evidence, we find that current static reflection methods may lead to redundant, drift, and stubborn issues. To mitigate this, we introduce **I**nstruct-**o**f-**R**eflec**t**ion (**IoRT**), a novel and general reflection framework that leverages dynamic-meta instruction to enhance the iterative reflection capability of LLMs. Specifically, we propose the instructor driven by the meta-thoughts and self-consistency classifier, generates various instructions, including refresh, stop, and select, to guide the next reflection iteration. Our experiments demonstrate that IoRT achieves an average improvement of 10.1% over established baselines in mathematical and commonsense reasoning tasks, highlighting its efficacy and applicability. Our code is available at https://github.com/llp635/IoRT.