Taming LLMs with Negative Samples: A Reference-Free Framework to Evaluate Presentation Content with Actionable Feedback

TL;DR

This paper introduces REFLEX, a reference-free evaluation framework for presentation content that uses negative samples to generate scores and actionable feedback, improving over existing methods.

Contribution

It presents a novel reference-free evaluation approach for presentation slides using negative samples and fine-tuned LLMs, along with a new benchmark dataset, RefSlides.

Findings

REFLEX outperforms classical heuristic-based evaluations.

The approach provides accurate scores and explanations.

It effectively characterizes presentation content properties.

Abstract

The generation of presentation slides automatically is an important problem in the era of generative AI. This paper focuses on evaluating multimodal content in presentation slides that can effectively summarize a document and convey concepts to a broad audience. We introduce a benchmark dataset, RefSlides, consisting of human-made high-quality presentations that span various topics. Next, we propose a set of metrics to characterize different intrinsic properties of the content of a presentation and present REFLEX, an evaluation approach that generates scores and actionable feedback for these metrics. We achieve this by generating negative presentation samples with different degrees of metric-specific perturbations and use them to fine-tune LLMs. This reference-free evaluation technique does not require ground truth presentations during inference. Our extensive automated and human experiments demonstrate that our evaluation approach outperforms classical heuristic-based and state-of-the-art large language model-based evaluations in generating scores and explanations.