Large Language Models (LLMs) are increasingly being utilized by both
candidates and employers in the recruitment context. However, with this comes
numerous ethical concerns, particularly related to the lack of transparency in
these "black-box" models. Although previous studies have sought to increase the
transparency of these models by investigating the personality traits of LLMs,
many of the previous studies have provided them with personality assessments to
complete. On the other hand, this study seeks to obtain a better understanding
of such models by examining their output variations based on different input
prompts. Specifically, we use a novel elicitation approach using prompts
derived from common interview questions, as well as prompts designed to elicit
particular Big Five personality traits to examine whether the models were
susceptible to trait-activation like humans are, to measure their personality
based on the language used in their outputs. To do so, we repeatedly prompted
multiple LMs with different parameter sizes, including Llama-2, Falcon,
Mistral, Bloom, GPT, OPT, and XLNet (base and fine tuned versions) and examined
their personality using classifiers trained on the myPersonality dataset. Our
results reveal that, generally, all LLMs demonstrate high openness and low
extraversion. However, whereas LMs with fewer parameters exhibit similar
behaviour in personality traits, newer and LMs with more parameters exhibit a
broader range of personality traits, with increased agreeableness, emotional
stability, and openness. Furthermore, a greater number of parameters is
positively associated with openness and conscientiousness. Moreover, fine-tuned
models exhibit minor modulations in their personality traits, contingent on the
dataset. Implications and directions for future research are discussed.

借助不同输入提示，使用多个参数大小的多个大型语言模型对它们的输出进行了评估，结果显示，大型语言模型普遍表现出高度的开放性和低度的外向性，且参数越多越倾向于开放性和责任心，并且在不同数据集上对细调模型产生了微小的调整

大规模语言模型中引发大五人格特质的文本分析：基于分类器驱动方法

Eliciting Big Five Personality Traits in Large Language Models: A  Textual Analysis with Classifier-Driven Approach

Compact neural network offers many benefits for real-world applications.
However, it is usually challenging to train the compact neural networks with
small parameter sizes and low computational costs to achieve the same or better
model performance compared to more complex and powerful architecture. This is
particularly true for multitask learning, with different tasks competing for
resources. We present a simple, efficient and effective multitask learning
overparameterisation neural network design by overparameterising the model
architecture in training and sharing the overparameterised model parameters
more effectively across tasks, for better optimisation and generalisation.
Experiments on two challenging multitask datasets (NYUv2 and COCO) demonstrate
the effectiveness of the proposed method across various convolutional networks
and parameter sizes.

紧凑型神经网络面向多任务学习的过参数化设计以提高优化和泛化性能，通过在训练过程中对模型架构进行过参数化，并更有效地共享过参数化的模型参数，以在小参数大小和低计算成本下获得与更复杂和强大架构相同或更好的模型性能。在两个具有挑战性的多任务数据集（NYUv2 和 COCO）上的实验证明了所提方法在各种卷积网络和参数大小下的有效性。