Multi Reference

Multi-reference evaluation aims to improve the accuracy and reliability of assessing text generation quality, particularly for large language models (LLMs), by leveraging multiple reference texts instead of relying on single, potentially biased, examples. Current research focuses on developing novel evaluation metrics that incorporate multiple references to better align with human judgment, addressing issues like data leakage and limited reference diversity, and enhancing interpretability through detailed error analysis. This work is significant because more robust evaluation methods are crucial for advancing LLM development and ensuring the responsible deployment of these powerful technologies across various applications, including machine translation and grammatical error correction.