# Norm Participation Grounds Language

arXiv: https://arxiv.org/abs/2206.02885 · HTML: https://mlsanity.com/paper/2206.02885/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 6 Jun 2022.

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [Experience Grounds Language](https://mlsanity.com/paper/2004.10151/) (2020)
- [A Review on Language Models as Knowledge Bases](https://mlsanity.com/paper/2204.06031/) (2022)
- [Toward a Perspectivist Turn in Ground Truthing for Predictive Computing](https://mlsanity.com/paper/2109.04270/) (2021)
- [BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief](https://mlsanity.com/paper/2109.14723/) (2021)
- [From partners to populations: A hierarchical Bayesian account of coordination and convention](https://mlsanity.com/paper/2104.05857/) (2021)
- [Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation](https://mlsanity.com/paper/2110.08501/) (2021)

## Built on by (most cited first, 4 of 4)

- [Language models show human-like content effects on reasoning tasks](https://mlsanity.com/paper/2207.07051/) (2022)
- [LLMs syntactically adapt their language use to their conversational partner](https://mlsanity.com/paper/2503.07457/) (2025)
- [Dealing with Semantic Underspecification in Multimodal NLP](https://mlsanity.com/paper/2306.05240/) (2023)
- [LLMs as Function Approximators: Terminology, Taxonomy, and Questions for Evaluation](https://mlsanity.com/paper/2407.13744/) (2024)

## 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.