A Reasoning and Value Alignment Test to Assess Advanced GPT Reasoning
Open accessAbstract
In response to diverse perspectives on artificial general intelligence (AGI), ranging from potential safety and ethical concerns to more extreme views about the threats it poses to humanity, this research presents a generic method to gauge the reasoning capabilities of artificial intelligence (AI) models as a foundational step in evaluating safety measures. Recognizing that AI reasoning measures cannot be wholly automated, due to factors such as cultural complexity, we conducted an extensive examination of five commercial generative pre-trained transformers (GPTs), focusing on their comprehension and interpretation of culturally intricate contexts. Utilizing our novel “Reasoning and Value Alignment Test,” we assessed the GPT models’ ability to reason in complex situations and grasp local cultural subtleties. Our findings have indicated that, although the models have exhibited high levels of human-like reasoning, significant limitations remained, especially concerning the interpretation of cultural contexts. This article also explored potential applications and use-cases of our Test, underlining its significance in AI training, ethics compliance, sensitivity auditing, and AI-driven cultural consultation. We concluded by emphasizing its broader implications in the AGI domain, highlighting the necessity for interdisciplinary approaches, wider accessibility to various GPT models, and a profound understanding of the interplay between GPT reasoning and cultural sensitivity.
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LLMs operate as black boxes with opaque internal mechanisms, making it difficult to interpret their decision-making processes and identify potential biases or ethical issues.
“Furthermore, LLMs operate as black boxes with opaque internal mechanisms, making it difficult to interpret their decision-making processes and identify potential biases or ethical issues [4], [27].”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [4]
LLMs like Llama and GPT-4 are capable of understanding and generating human-like text, performing tasks ranging from translation to creative writing.
“LLMs like Llama and GPT-4 are capable of understanding and generating human-like text, performing tasks ranging from translation to creative writing [1], [4].”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [4]
LLM benchmarks are constrained by the current limits of the benchmark creators' human knowledge, hindering their ability to fully assess and cultivate emerging AI capabilities that may surpass conventional human understanding, potentially stalling innovation in fields that rely on advanced AI insights.
“LLM benchmarks are constrained by the current limits of the benchmark creators' human knowledge, hindering their ability to fully assess and cultivate emerging AI capabilities that may surpass conventional human understanding, potentially stalling innovation in fields that rely on advanced AI insights [4].”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [4]
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References 60 · Cited by 42
- Authors
- Mcintosh, T. R., Liu, T., Susnjak, T., Watters, P., & Halgamuge, M. N.
- Affiliations
- La Trobe University (AU)
- Massey University (NZ)
- Massey University, Albany, New Zealand
- Cyberstronomy Pty Ltd, Ballarat, Australia
- RMIT University (AU)
- Venue
- ACM Transactions on Interactive Intelligent Systems
- Published
- 2024-08-02
- Volume
- 14
- Issue
- 3
- Pages
- 1-37
- ISSN
- 2160-6455 (ISSN-L)
- 2160-6463
- OpenAlex
- W4399285823
- Cited by
- 42
- References
- 60
- Citations by year
- 2026: 21 · 2025: 10 · 2024: 11
- FWCI
- 17.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Transactions on Interactive Intelligent Systems (publishedVersion, cc-by-nc-sa)
- Figshare (submittedVersion, cc-by-nc-sa)
- La Trobe University
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- cultural sensitivity, AI safety evaluation, value alignment, generative pre-trained transformer, AI training
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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