Large Language Models (LLMs) have demonstrated their efficacy across a broad
spectrum of tasks in healthcare applications. However, often LLMs need to be
fine-tuned on task-specific expert annotated data to achieve optimal
performance, which can be expensive and time consuming. In this study, we
fine-tune PaLM-2 with parameter efficient fine-tuning (PEFT) using noisy labels
obtained from gemini-pro 1.0 for the detection of Schedule-of-Event (SoE)
tables, which specify care plan in clinical trial protocols. We introduce a
filtering mechanism to select high-confidence labels for this table
classification task, thereby reducing the noise in the auto-generated labels.
We show that fine-tuned PaLM-2 with those labels achieves performance that
exceeds the gemini-pro 1.0 and other LLMs. Furthermore, its performance is
close to a PaLM-2 fine-tuned on labels obtained from non-expert annotators. Our
results show that leveraging LLM-generated labels through powerful models like
gemini-pro can potentially serve as a viable strategy for improving LLM
performance through fine-tuning in specialized tasks, particularly in domains
where expert annotations are scarce, expensive, or time-consuming to obtain.

通过使用 gemini-pro 1.0 生成的噪声标签，使用参数高效的 fine-tuning（PEFT）对 PaLM-2 进行微调，减少自动生成标签的噪声，从而在临床试验方案的 Schedule-of-Event 表的检测中，在超过 gemini-pro 1.0 和其他 LLMs 的性能的基础上，接近于使用非专家注释者获得的标签对 PaLM-2 进行微调的性能。

利用 LLM 标记数据进行有选择的微调可能减少对人工注释的依赖：以事件表检测为例的案例研究

Selective Fine-tuning on LLM-labeled Data May Reduce Reliance on Human  Annotation: A Case Study Using Schedule-of-Event Table Detection

The success of Large Language Models (LLMs) has led to a parallel rise in the
development of Large Multimodal Models (LMMs), such as Gemini-pro, which have
begun to transform a variety of applications. These sophisticated multimodal
models are designed to interpret and analyze complex data, integrating both
textual and visual information on a scale previously unattainable, opening new
avenues for a range of applications. This paper investigates the applicability
and effectiveness of prompt-engineered Gemini-pro LMMs versus fine-tuned Vision
Transformer (ViT) models in addressing critical security challenges. We focus
on two distinct tasks: a visually evident task of detecting simple triggers,
such as small squares in images, indicative of potential backdoors, and a
non-visually evident task of malware classification through visual
representations. Our results highlight a significant divergence in performance,
with Gemini-pro falling short in accuracy and reliability when compared to
fine-tuned ViT models. The ViT models, on the other hand, demonstrate
exceptional accuracy, achieving near-perfect performance on both tasks. This
study not only showcases the strengths and limitations of prompt-engineered
LMMs in cybersecurity applications but also emphasizes the unmatched efficacy
of fine-tuned ViT models for precise and dependable tasks.

研究了用于解决关键安全挑战的提示引擎 Gemini-pro 多模态模型和精调 ViT 模型的适用性和效果，发现 Gemini-pro 在准确性和可靠性方面存在明显差异，而精调的 ViT 模型在两个任务上表现出色，达到近乎完美的性能。