A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations
This addresses the need for standardized evaluation methods in the LLM community to ensure reliable deployment in real-world applications, but it is incremental as it synthesizes existing knowledge rather than introducing new techniques.
The paper tackles the problem of inconsistent and unreliable evaluations of Large Language Models (LLMs) by systematically reviewing challenges and limitations, resulting in recommendations for reproducible and robust evaluations.
Large Language Models (LLMs) have recently gained significant attention due to their remarkable capabilities in performing diverse tasks across various domains. However, a thorough evaluation of these models is crucial before deploying them in real-world applications to ensure they produce reliable performance. Despite the well-established importance of evaluating LLMs in the community, the complexity of the evaluation process has led to varied evaluation setups, causing inconsistencies in findings and interpretations. To address this, we systematically review the primary challenges and limitations causing these inconsistencies and unreliable evaluations in various steps of LLM evaluation. Based on our critical review, we present our perspectives and recommendations to ensure LLM evaluations are reproducible, reliable, and robust.