# Evidence of interrelated cognitive-like capabilities in large language models: Indications of artificial general intelligence or achievement?

arXiv: https://arxiv.org/abs/2310.11616 · HTML: https://mlsanity.com/paper/2310.11616/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 91 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [MathPrompter: Mathematical Reasoning using Large Language Models](https://mlsanity.com/paper/2303.05398/) (2023)
- [How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition](https://mlsanity.com/paper/2310.05492/) (2023)
- [WinoGrande: An Adversarial Winograd Schema Challenge at Scale](https://mlsanity.com/paper/1907.10641/) (2019)
- [How well do Large Language Models perform in Arithmetic tasks?](https://mlsanity.com/paper/2304.02015/) (2023)
- [A Primer on Neural Network Models for Natural Language Processing](https://mlsanity.com/paper/1510.00726/) (2015)
- [Limitations of Language Models in Arithmetic and Symbolic Induction](https://mlsanity.com/paper/2208.05051/) (2022)
- [Model Complexity of Deep Learning: A Survey](https://mlsanity.com/paper/2103.05127/) (2021)
- [Metrology for AI: From Benchmarks to Instruments](https://mlsanity.com/paper/1911.01875/) (2019)

## Built on by (most cited first, 27 of 27)

- [Observational Scaling Laws and the Predictability of Language Model Performance](https://mlsanity.com/paper/2405.10938/) (2024)
- [Measuring what Matters: Construct Validity in Large Language Model Benchmarks](https://mlsanity.com/paper/2511.04703/) (2025)
- [Evaluating General-Purpose AI with Psychometrics](https://mlsanity.com/paper/2310.16379/) (2023)
- [Scales++: Compute Efficient Evaluation Subset Selection with Cognitive Scales Embeddings](https://mlsanity.com/paper/2510.26384/) (2025)
- [Collaborative Performance Prediction for Large Language Models](https://mlsanity.com/paper/2407.01300/) (2024)
- [From Benchmarks to Skills: Low-Rank Factors for LLM Evaluation](https://mlsanity.com/paper/2507.20208/) (2025)
- [The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarks](https://mlsanity.com/paper/2410.07391/) (2024)
- [A Neuropsychologically Grounded Evaluation of LLM Cognitive Abilities](https://mlsanity.com/paper/2603.02540/) (2026)
- [Deployment Decision Reliability: A Generalizability-Theory Framework for Sizing Long-Horizon Agent Evaluations](https://mlsanity.com/paper/2608.11323/) (2026)
- [Automated Capability Evaluation of Foundation Models](https://mlsanity.com/paper/2505.17228/) (2025)
- [Beyond Divergent Creativity: A Human-Based Evaluation of Creativity in Large Language Models](https://mlsanity.com/paper/2601.20546/) (2026)
- [You Don't Need to Run Every Eval](https://mlsanity.com/paper/2606.24020/) (2026)
- [Position: AI Evaluations Should be Grounded on a Theory of Capability](https://mlsanity.com/paper/2509.19590/) (2025)
- [Cost-Efficient Estimation of General Abilities Across Benchmarks](https://mlsanity.com/paper/2604.01418/) (2026)
- [The Rise and Fall of G in AGI](https://mlsanity.com/paper/2604.09911/) (2026)
- [Evaluating Cognitive Age Alignment in Interactive AI Agents](https://mlsanity.com/paper/2605.17894/) (2026)
- [Project MPG: towards a generalized performance benchmark for LLM capabilities](https://mlsanity.com/paper/2410.22368/) (2024)
- [Pressure Reveals Character: Behavioural Alignment Evaluation at Depth](https://mlsanity.com/paper/2602.20813/) (2026)
- [Dynamic LLM Routers are Often Misguided](https://mlsanity.com/paper/2610.02762/) (2026)
- [On the Measure of a Model: From Intelligence to Generality](https://mlsanity.com/paper/2511.11773/) (2025)
- [Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI](https://mlsanity.com/paper/2511.18517/) (2025)
- [An Interpretable and Scalable Framework for Evaluating Large Language Models](https://mlsanity.com/paper/2605.07046/) (2026)
- [The Metanym Game: An LLM Benchmark Without Ground Truth That Rises With the Models It Measures](https://mlsanity.com/paper/2606.21008/) (2026)
- [Will Scaling Improve Social Simulation with LLMs?](https://mlsanity.com/paper/2607.02464/) (2026)
- [CogArena: A Multimethod Evaluation of Cognitive Ability Structure in Large Language Models](https://mlsanity.com/paper/2607.24999/) (2026)
- [When benchmark inferences do not compose: Projectibility in AI evaluation](https://mlsanity.com/paper/2607.26159/) (2026)
- [One Capability or Many? Structural and Predictive Tests of Benchmark Validity Disagree About Economic Benchmarks for Frontier AI](https://mlsanity.com/paper/2608.29420/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are advanced artificial intelligence (AI) systems that can perform a variety of tasks commonly found in human intelligence tests, such as defining words, performing calculations, and engaging in verbal reasoning. There are also substantial individual differences in LLM capacities. Given the consistent observation of a positive manifold and general intelligence factor in human samples, along with group-level factors (e.g., crystallized intelligence), we hypothesized that LLM test scores may also exhibit positive intercorrelations, which could potentially give rise to an artificial general ability (AGA) factor and one or more group-level factors. Based on a sample of 591 LLMs and scores from 12 tests aligned with fluid reasoning (Gf), domain-specific knowledge (Gkn), reading/writing (Grw), and quantitative knowledge (Gq), we found strong empirical evidence for a positive manifold and a general factor of ability. Additionally, we identified a combined Gkn/Grw group-level factor. Finally, the number of LLM parameters correlated positively with both general factor of ability and Gkn/Grw factor scores, although the effects showed diminishing returns. We interpreted our results to suggest that LLMs, like human cognitive abilities, may share a common underlying efficiency in processing information and solving problems, though whether LLMs manifest primarily achievement/expertise rather than intelligence remains to be determined. Finally, while models with greater numbers of parameters exhibit greater general cognitive-like abilities, akin to the connection between greater neuronal density and human general intelligence, other characteristics must also be involved.