# Large language models and linguistic intentionality

arXiv: https://arxiv.org/abs/2404.09576 · HTML: https://mlsanity.com/paper/2404.09576/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 64 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Transformer Feed-Forward Layers Are Key-Value Memories](https://mlsanity.com/paper/2012.14913/) (2020)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [What you can cram into a single vector: Probing sentence embeddings for linguistic properties](https://mlsanity.com/paper/1805.01070/) (2018)
- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019)
- [Distributional Semantics and Linguistic Theory](https://mlsanity.com/paper/1905.01896/) (2019)
- [The Vector Grounding Problem](https://mlsanity.com/paper/2304.01481/) (2023)
- [Do Language Models' Words Refer?](https://mlsanity.com/paper/2308.05576/) (2023)
- [Language Writ Large: LLMs, ChatGPT, Grounding, Meaning and Understanding](https://mlsanity.com/paper/2402.02243/) (2024)
- [Are Language Models More Like Libraries or Like Librarians? Bibliotechnism, the Novel Reference Problem, and the Attitudes of LLMs](https://mlsanity.com/paper/2401.04854/) (2024)
- [Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition](https://mlsanity.com/paper/2108.11857/) (2021)
- [TOKEN is a MASK: Few-shot Named Entity Recognition with Pre-trained Language Models](https://mlsanity.com/paper/2206.07841/) (2022)

## Built on by (most cited first, 6 of 6)

- [People cannot distinguish GPT-4 from a human in a Turing test](https://mlsanity.com/paper/2405.08007/) (2024)
- [Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Models](https://mlsanity.com/paper/2412.17128/) (2024)
- [Do Large Language Models Advocate for Inferentialism?](https://mlsanity.com/paper/2412.14501/) (2024)
- [Asymmetric Communication: Large Language Models and Language Games](https://mlsanity.com/paper/2607.28137/) (2026)
- [Grounding Without Corrective Control: Truth-Tracking Profiles for Large Language Models](https://mlsanity.com/paper/2608.14252/) (2026)
- [Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations](https://mlsanity.com/paper/2609.38486/) (2026)

## Abstract (arXiv)

Do large language models like Chat-GPT or LLaMa meaningfully use the words they produce? Or are they merely clever prediction machines, simulating language use by producing statistically plausible text? There have already been some initial attempts to answer this question by showing that these models meet the criteria for entering meaningful states according to metasemantic theories of mental content. In this paper, I will argue for a different approach - that we should instead consider whether language models meet the criteria given by our best metasemantic theories of linguistic content. In that vein, I will illustrate how this can be done by applying two such theories to the case of language models: Gareth Evans' (1982) account of naming practices and Ruth Millikan's (1984, 2004, 2005) teleosemantics. In doing so, I will argue that it is a mistake to think that the failure of LLMs to meet plausible conditions for mental intentionality thereby renders their outputs meaningless, and that a distinguishing feature of linguistic intentionality - dependency on a pre-existing linguistic system - allows for the plausible result LLM outputs are meaningful.