Norm Participation Grounds Language
Not on the map. Why?
Abstract (arXiv)
The striking recent advances in eliciting seemingly meaningful language behaviour from language-only machine learning models have only made more apparent, through the surfacing of clear limitations, the need to go beyond the language-only mode and to ground these models "in the world". Proposals for doing so vary in the details, but what unites them is that the solution is sought in the addition of non-linguistic data types such as images or video streams, while largely keeping the mode of learning constant. I propose a different, and more wide-ranging conception of how grounding should be understood: What grounds language is its normative nature. There are standards for doing things right, these standards are public and authoritative, while at the same time acceptance of authority can and must be disputed and negotiated, in interactions in which only bearers of normative status can rightfully participate. What grounds language, then, is the determined use that language users make of it, and what it is grounded in is the community of language users. I sketch this idea, and draw some conclusions for work on computational modelling of meaningful language use.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 49 references that are in the map. First posted on arXiv 6 Jun 2022.
Builds on
8 of 49 references.
References · 8
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2014Show and Tell: A Neural Image Caption Generatorcited by 877
- 2020Experience Grounds Languagecited by 241
- 2022A Review on Language Models as Knowledge Basescited by 156
- 2021Toward a Perspectivist Turn in Ground Truthing for Predictive Computingcited by 129
- 2021BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Beliefcited by 56
- 2021From partners to populations: A hierarchical Bayesian account of coordination and conventioncited by 27
- 2021Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generationcited by 26
Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1.
4 papers.
- 2022Language models show human-like content effects on reasoning taskscited by 171
- 2025LLMs syntactically adapt their language use to their conversational partnercited by 15
- 2023Dealing with Semantic Underspecification in Multimodal NLPcited by 9
- 2024LLMs as Function Approximators: Terminology, Taxonomy, and Questions for Evaluationcited by 0
- 2025LLMs syntactically adapt their language use to their conversational partner10 Mar 2025
- 2024LLMs as Function Approximators: Terminology, Taxonomy, and Questions for Evaluation18 Jul 2024
- 2023Dealing with Semantic Underspecification in Multimodal NLP8 Jun 2023
- 2022Language models show human-like content effects on reasoning tasks14 Jul 2022