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From grading papers to summarizing medical documents, large language models(LLMs) are evermore used for evaluation of text generated by humans and AIalike. However, despite their extensive utility, LLMs exhibit distinct failuremodes, necessitating a thorough audit and improvement of their text evaluationcapabilities. Here we introduce ALLURE, a systematic approach to Auditing LargeLanguage Models Understanding and Reasoning Errors. ALLURE involves comparingLLM-generated evaluations with annotated data, and iteratively incorporatinginstances of significant deviation into the evaluator, which leveragesin-context learning (ICL) to enhance and improve robust evaluation of text byLLMs. Through this iterative process, we refine the performance of theevaluator LLM, ultimately reducing reliance on human annotators in theevaluation process. We anticipate ALLURE to serve diverse applications of LLMsin various domains related to evaluation of textual data, such as medicalsummarization, education, and and productivity.

ALLURE: Auditing and Improving LLM-based Evaluation of Text using Iterative In-Context-Learning