# Inflation Attitudes of Large Language Models

arXiv: https://arxiv.org/abs/2512.14306 · HTML: https://mlsanity.com/paper/2512.14306/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 79 references that are in the map.
- First posted on arXiv 16 Dec 2025.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions](https://mlsanity.com/paper/2308.11483/) (2023)
- [LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations](https://mlsanity.com/paper/2410.02707/) (2024)
- [OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization](https://mlsanity.com/paper/2212.12017/) (2022)
- [Probing Pre-Trained Language Models for Cross-Cultural Differences in Values](https://mlsanity.com/paper/2203.13722/) (2022)
- [Large language models surpass human experts in predicting neuroscience results](https://mlsanity.com/paper/2403.03230/) (2024)
- [LLM economicus? Mapping the Behavioral Biases of LLMs via Utility Theory](https://mlsanity.com/paper/2408.02784/) (2024)
- [Large Language Models: An Applied Econometric Framework](https://mlsanity.com/paper/2412.07031/) (2024)
- [The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarks](https://mlsanity.com/paper/2410.07391/) (2024)
- [Comparing Human and LLM Generated Code: The Jury is Still Out!](https://mlsanity.com/paper/2501.16857/) (2025)
- [From interpretability to inference: an estimation framework for universal approximators](https://mlsanity.com/paper/1903.04209/) (2019)
- [Revealing economic facts: LLMs know more than they say](https://mlsanity.com/paper/2505.08662/) (2025)
- [Instruction Tuning Chronologically Consistent Language Models](https://mlsanity.com/paper/2510.11677/) (2025)

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

- [HouseholdBench: Evaluating Large Language Models as Predictors of Household Economic Behavior](https://mlsanity.com/paper/2610.07563/) (2026)

## Abstract (arXiv)

This paper investigates the ability of Large Language Models (LLMs), specifically GPT-3.5-turbo (GPT), to form inflation perceptions and expectations based on macroeconomic price signals. We compare the LLM's output to household survey data and official statistics, mimicking the information set and demographic characteristics of the Bank of England's Inflation Attitudes Survey (IAS). Our quasi-experimental design exploits the timing of GPT's training cut-off in September 2021 which means it has no knowledge of the subsequent UK inflation surge. We find that GPT tracks aggregate survey projections and official statistics at short horizons. At a disaggregated level, GPT replicates key empirical regularities of households' inflation perceptions, particularly for income, housing tenure, and social class. A novel Shapley value decomposition of LLM outputs suited for the synthetic survey setting provides well-defined insights into the drivers of model outputs linked to prompt content. We find that GPT demonstrates a heightened sensitivity to food inflation information similar to that of human respondents. However, we also find that it lacks a consistent model of consumer price inflation. More generally, our approach could be used to evaluate the behaviour of LLMs for use in the social sciences, to compare different models, or to assist in survey design.