mlsanityData to 14 Oct 2026

Grounding for Artificial Intelligence

arXiv 2312.09532 ↗First posted 15 Dec 2023cs.AIcited by 2

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Abstract (arXiv)

A core function of intelligence is grounding, which is the process of connecting the natural language and abstract knowledge to the internal representation of the real world in an intelligent being, e.g., a human. Human cognition is grounded in our sensorimotor experiences in the external world and subjective feelings in our internal world. We use languages to communicate with each other and the languages are grounded on our shared sensorimotor experiences and feelings. Without this shard grounding, it is impossible for us to understand each other because all natural languages are highly abstract and are only able to describe a tiny portion of what has happened or is happening in the real world. Although grounding at high or abstract levels has been studied in different fields and applications, to our knowledge, limited systematic work at fine-grained levels has been done. With the rapid progress of large language models (LLMs), it is imperative that we have a sound understanding of grounding in order to move to the next level of intelligence. It is also believed that grounding is necessary for Artificial General Intelligence (AGI). This paper makes an attempt to systematically study this problem.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 15 Dec 2023.

Builds on

7 of 24 references.

References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2020Experience Grounds Languagecited by 242
  5. 2021Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand?cited by 55
  6. 2020Word meaning in minds and machinescited by 51
  7. 2020WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challengecited by 43

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.