# The Outputs of Large Language Models are Meaningless

arXiv: https://arxiv.org/abs/2509.22206 · HTML: https://mlsanity.com/paper/2509.22206/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [Meaning without reference in large language models](https://mlsanity.com/paper/2208.02957/) (2022)
- [A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks](https://mlsanity.com/paper/2306.07303/) (2023)
- [Word meaning in minds and machines](https://mlsanity.com/paper/2008.01766/) (2020)
- [A Philosophical Introduction to Language Models - Part II: The Way Forward](https://mlsanity.com/paper/2405.03207/) (2024)
- [Do Language Models' Words Refer?](https://mlsanity.com/paper/2308.05576/) (2023)

## Built on by (most cited first, 1 of 1)

- [Chatbots Output Meaningful (but Problematic) Language](https://mlsanity.com/paper/2606.02973/) (2026)

## Abstract (arXiv)

In this paper, we offer a simple argument for the conclusion that the outputs of large language models (LLMs) are meaningless. Our argument is based on two key premises: (a) that certain kinds of intentions are needed in order for LLMs' outputs to have literal meanings, and (b) that LLMs cannot plausibly have the right kinds of intentions. We defend this argument from various types of responses, for example, the semantic externalist argument that deference can be assumed to take the place of intentions and the semantic internalist argument that meanings can be defined purely in terms of intrinsic relations between concepts, such as conceptual roles. We conclude the paper by discussing why, even if our argument is sound, the outputs of LLMs nevertheless seem meaningful and can be used to acquire true beliefs and even knowledge.