Introduction
The assessment of LLMs is a crucial endeavor as it influences further development and understanding of AI capabilities. Current research in this domain has afforded considerable attention to the tasks LLMs should undertake and the domains they should encompass; yet the methodologies for evaluation have been comparatively underexplored. This paper aims to address this gap by thoroughly examining evaluation methods, encompassing criteria selection, annotation methods, and ranking systems. With an expansive paper involving 20 LLMs, over 2,000 individuals, and a combination of manual and automatic evaluations generating substantial data, this research provides a framework and actionable insights into how best to evaluate LLMs.
Studies and Methodology
Diverse methodologies across previous works have established varied approaches for LLM evaluation. In contrast to automated evaluations championed by studies like HELM and MMLU, this research underscores the significance of manual evaluations, drawing from a comprehensive pool of annotators. The paper spotlights both the strengths and limitations of manual evaluations compared to LLM-based automatic evaluations like BERTScore and discusses the nuances introduced by public versus onsite annotators.
The aptly named LLMEval dataset is the cornerstone of this comparison, delivering a novel resource for evaluating a multitude of LLMs across a broad spectrum with the promise of unbiased results, thanks to a double-blind testing approach. Furthermore, by comparatively analyzing different scoring criteria—from informativeness to fluency—this paper carves out a nuanced picture of what truly distinguishes LLMs in performance assessments.
Results and Discussion
Unveiling the findings, this paper reports that onsite evaluations outperform other manual formats in accuracy, suggesting this as the preferable route for future studies. Intriguingly, the findings also suggest that automated evaluations have considerable merit, particularly with regard to handling voluminous tasks—yet the divide between automated and manual methods is most evident in the subjective dimension. As expected, public annotations are less reliable due to their lower accuracy and consistency, reinforcing the value of expert involvement in the evaluative process.
Pertaining to ranking systems, the paper critically analyzes Elo rating versus Points scoring systems, with important implications for using these systems within LLM evaluations. The Elo system, despite its repute in chess, demonstrates fluctuations that negate its stability for LLM evaluations—a key insight for researchers deploying competitive pairwise evaluations.
Concluding Remarks
The paper's contributions are manifold. Importantly, it catalyzes the ongoing discourse on the 'how' of LLM evaluation, a conversation far from mature in the AI community. Additionally, LLMEval offers a repository that's invaluable for future research, allowing for variation in evaluation approaches. Coupled with ten succinctly presented conclusions stemming from the elaborate comparison between different evaluation formats, this paper advances the scholarly community’s tools for dissecting and understanding the performance of LLMs.
It also pens a path forward for subsequent evaluations, emphasizing the premium combination of informativeness and accuracy as differentiators. Recognizing the complementary nature of automated and manual evaluations, the paper argues for their concurrent usage, especially since automated methods align reasonably with human judgment. As AI systems veer closer to human-like cognition, these insights are imperative, ensuring that evaluative processes evolve in tandem with technological developments.