Recent studies have demonstrated the emerging capabilities of foundation
models like ChatGPT in several fields, including affective computing. However,
accessing these emerging capabilities is facilitated through prompt
engineering. Despite the existence of some prompting techniques, the field is
still rapidly evolving and many prompting ideas still require investigation. In
this work, we introduce a method to evaluate and investigate the sensitivity of
the performance of foundation models based on different prompts or generation
parameters. We perform our evaluation on ChatGPT within the scope of affective
computing on three major problems, namely sentiment analysis, toxicity
detection, and sarcasm detection. First, we carry out a sensitivity analysis on
pivotal parameters in auto-regressive text generation, specifically the
temperature parameter $T$ and the top-$p$ parameter in Nucleus sampling,
dictating how conservative or creative the model should be during generation.
Furthermore, we explore the efficacy of several prompting ideas, where we
explore how giving different incentives or structures affect the performance.
Our evaluation takes into consideration performance measures on the affective
computing tasks, and the effectiveness of the model to follow the stated
instructions, hence generating easy-to-parse responses to be smoothly used in
downstream applications.

通过敏感度分析和评估不同提示或生成参数对 foundation models 的性能敏感程度，探索 affective computing 领域中的 prompting 技术，以及对情感分析、毒性检测和讽刺检测等任务的性能影响。

关于 ChatGPT 在情感计算中的提示敏感性

On Prompt Sensitivity of ChatGPT in Affective Computing

Scene graphs provide structured semantic understanding beyond images. For
downstream tasks, such as image retrieval, visual question answering, visual
relationship detection, and even autonomous vehicle technology, scene graphs
can not only distil complex image information but also correct the bias of
visual models using semantic-level relations, which has broad application
prospects. However, the heavy labour cost of constructing graph annotations may
hinder the application of PSG in practical scenarios. Inspired by the
observation that people usually identify the subject and object first and then
determine the relationship between them, we proposed to decouple the scene
graphs generation task into two sub-tasks: 1) an image segmentation task to
pick up the qualified objects. 2) a restricted auto-regressive text generation
task to generate the relation between given objects. Therefore, in this work,
we introduce image semantic relation generation (ISRG), a simple but effective
image-to-text model, which achieved 31 points on the OpenPSG dataset and
outperforms strong baselines respectively by 16 points (ResNet-50) and 5 points
(CLIP).

为解决场景图构建的大量人工成本，提出了一种基于图像语义关系生成的简单有效的图像到文本模型（ISRG），该模型通过将场景图任务分解为两个子任务，即图像分割任务和限制性自回归文本生成任务，极大地降低了场景图的构建成本。在 OpenPSG 数据集上，该模型获得了 31 分，相应地优于强基线 ResNet-50 和 CLIP 16 点和 5 点。