mlsanityData to 13 Oct 2026

Rethinking the Evaluating Framework for Natural Language Understanding in AI Systems: Language Acquisition as a Core for Future Metrics

arXiv 2309.11981 ↗First posted 21 Sep 2023cs.CL cs.AIcited by 2

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

In the burgeoning field of artificial intelligence (AI), the unprecedented progress of large language models (LLMs) in natural language processing (NLP) offers an opportunity to revisit the entire approach of traditional metrics of machine intelligence, both in form and content. As the realm of machine cognitive evaluation has already reached Imitation, the next step is an efficient Language Acquisition and Understanding. Our paper proposes a paradigm shift from the established Turing Test towards an all-embracing framework that hinges on language acquisition, taking inspiration from the recent advancements in LLMs. The present contribution is deeply tributary of the excellent work from various disciplines, point out the need to keep interdisciplinary bridges open, and delineates a more robust and sustainable approach.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 23 of its 137 references that are in the map. First posted on arXiv 21 Sep 2023.

Builds on

23 of 137 references.

References · 23

  1. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,467
  2. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,985
  3. 2019Unmasking Clever Hans Predictors and Assessing What Machines Really Learncited by 361
  4. 2021Modern Koopman Theory for Dynamical Systemscited by 273
  5. 2020Experience Grounds Languagecited by 241
  6. 2019Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methodscited by 62
  7. 2020Sophisticated Inferencecited by 56
  8. 2019Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methodscited by 55
  9. 2022Large Language Models and the Reverse Turing Testcited by 38
  10. 2021Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systemscited by 36
  11. 2022Designing Ecosystems of Intelligence from First Principlescited by 23
  12. 2021Machine learning on small size samples: A synthetic knowledge synthesiscited by 11
11 more
  1. 2020When we can trust computers (and when we can't)cited by 4
  2. 2021A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robotscited by 4
  3. 2022Building Human-like Communicative Intelligence: A Grounded Perspectivecited by 4
  4. 2023Symmetry and Complexity in Object-Centric Deep Active Inference Modelscited by 4
  5. 2016Moving Beyond the Turing Test with the Allen AI Science Challengecited by 3
  6. 2022Generalizable Neuro-symbolic Systems for Commonsense Question Answeringcited by 3
  7. 2021Towards A Measure Of General Machine Intelligencecited by 2
  8. 2022Emergent Linguistic Structures in Neural Networks are Fragilecited by 2
  9. 2023Can I say, now machines can think?cited by 2
  10. 2021Intelligence as information processing: brains, swarms, and computerscited by 1
  11. 2022In the realm of hybrid Brain: Human Brain and AIcited by 1

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Evaluating Cultural Knowledge Processing in Large Language Models: A Cognitive Benchmarking Framework Integrating Retrieval-Augmented Generationcited by 0
  2. 2026Lexical Consensus: Grounded Word Learning and Shared Meaning in Artificial Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debates
  2. 2023Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models
  3. 2025The illusion of a perfect metric: Why evaluating AI's words is harder than it looks
  4. 2023Establishing Trustworthiness: Rethinking Tasks and Model Evaluation
  5. 2026Conversational Agents and the Understanding of Human Language: Reflections on AI, LLMs, and Cognitive Science
  6. 2026Understanding Large Language Models
  7. 2023Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks
  8. 2026Measuring AI Reasoning: A Guide for Researchers