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Do LLM Personas Dream of Bull Markets? Comparing Human and AI Investment Strategies Through the Lens of the Five-Factor Model [0.3495246564946556]
Large Language Models (LLMs) have demonstrated the ability to adopt a personality and behave in a human-like manner. This study investigated whether an LLM persona with a specific Big Five personality profile would perform an investment task similarly to a human with the same personality traits. We found that LLMs are able to generalise traits into expected behaviours in three areas: learning style, impulsivity and risk appetite.
arXiv Detail & Related papers (2024-10-28T02:50:41Z)
Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis [7.098487130130114]
Risk analysis principles are context-less. Risk analysis requires a vast knowledge of national and international regulations and standards. Large language models can quickly summarize information in less time than a human and can be fine-tuned to specific tasks.
arXiv Detail & Related papers (2024-06-11T19:20:27Z)
Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context [5.361970694197912]
This paper proposes a framework, grounded in behavioral economics, to evaluate the decision-making behaviors of large language models (LLMs) We estimate the degree of risk preference, probability weighting, and loss aversion in a context-free setting for three commercial LLMs: ChatGPT-4.0-Turbo, Claude-3-Opus, and Gemini-1.0-pro. Our results reveal that LLMs generally exhibit patterns similar to humans, such as risk aversion and loss aversion, with a tendency to overweight small probabilities.
arXiv Detail & Related papers (2024-06-10T02:14:19Z)
On the Societal Impact of Open Foundation Models [93.67389739906561]
We focus on open foundation models, defined here as those with broadly available model weights. We identify five distinctive properties of open foundation models that lead to both their benefits and risks.
arXiv Detail & Related papers (2024-02-27T16:49:53Z)
LLMs Simulate Big Five Personality Traits: Further Evidence [51.13560635563004]
We analyze the personality traits simulated by Llama2, GPT4, and Mixtral. This contributes to the broader understanding of the capabilities of LLMs to simulate personality traits.
arXiv Detail & Related papers (2024-01-31T13:45:25Z)
Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias [57.42417061979399]
Recent studies show that instruction tuning (IT) and reinforcement learning from human feedback (RLHF) improve the abilities of large language models (LMs) dramatically. In this work, we investigate the effect of IT and RLHF on decision making and reasoning in LMs. Our findings highlight the presence of these biases in various models from the GPT-3, Mistral, and T5 families.
arXiv Detail & Related papers (2023-08-01T01:39:25Z)
Personality testing of Large Language Models: Limited temporal stability, but highlighted prosociality [0.0]
Large Language Models (LLMs) continue to gain popularity due to their human-like traits and the intimacy they offer to users. This study aimed to assess the temporal stability and inter-rater agreement on their responses on personality instruments in two time points. The findings revealed varying levels of inter-rater agreement in the LLMs responses over a short time.
arXiv Detail & Related papers (2023-06-07T10:14:17Z)
Revisiting the Reliability of Psychological Scales on Large Language Models [62.57981196992073]
This study aims to determine the reliability of applying personality assessments to Large Language Models. Analysis of 2,500 settings per model, including GPT-3.5, GPT-4, Gemini-Pro, and LLaMA-3.1, reveals that various LLMs show consistency in responses to the Big Five Inventory.
arXiv Detail & Related papers (2023-05-31T15:03:28Z)
Confounder Identification-free Causal Visual Feature Learning [84.28462256571822]
We propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders. CICF models the interventions among different samples based on front-door criterion, and then approximates the global-scope intervening effect upon the instance-level interventions. We uncover the relation between CICF and the popular meta-learning strategy MAML, and provide an interpretation of why MAML works from the theoretical perspective.
arXiv Detail & Related papers (2021-11-26T10:57:47Z)
Two steps to risk sensitivity [4.974890682815778]
conditional value-at-risk (CVaR) is a risk measure for modeling human and animal planning. We adopt a conventional distributional approach to CVaR in a sequential setting and reanalyze the choices of human decision-makers. We then consider a further critical property of risk sensitivity, namely time consistency, showing alternatives to this form of CVaR.
arXiv Detail & Related papers (2021-11-12T16:27:47Z)
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models [64.03398193325572]
Inference attacks against Machine Learning (ML) models allow adversaries to learn about training data, model parameters, etc. We concentrate on four attacks - namely, membership inference, model inversion, attribute inference, and model stealing. Our analysis relies on a modular re-usable software, ML-Doctor, which enables ML model owners to assess the risks of deploying their models.
arXiv Detail & Related papers (2021-02-04T11:35:13Z)

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