# Can Language Models Represent the Past without Anachronism?

arXiv: https://arxiv.org/abs/2505.00030 · HTML: https://mlsanity.com/paper/2505.00030/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning](https://mlsanity.com/paper/2404.05868/) (2024)
- [S3: Social-network Simulation System with Large Language Model-Empowered Agents](https://mlsanity.com/paper/2307.14984/) (2023)
- [Surveying the Dead Minds: Historical-Psychological Text Analysis with Contextualized Construct Representation (CCR) for Classical Chinese](https://mlsanity.com/paper/2403.00509/) (2024)

## Built on by (most cited first, 14 of 14)

- [Meaning Is Not A Metric: Using LLMs to make cultural context legible at scale](https://mlsanity.com/paper/2505.23785/) (2025)
- [Computational Hermeneutics: Evaluating generative AI as a cultural technology](https://mlsanity.com/paper/2604.16403/) (2026)
- [AI as Entertainment](https://mlsanity.com/paper/2601.08768/) (2026)
- [A Language Model from 1913: Pretraining on Historical Text](https://mlsanity.com/paper/2606.02991/) (2026)
- [Do Language Models Consistently Encode the Current Year?](https://mlsanity.com/paper/2608.15507/) (2026)
- [Stabilising Generative Models of Attitude Change](https://mlsanity.com/paper/2604.19791/) (2026)
- [On the Cultural Anachronism and Temporal Reasoning in Vision Language Models](https://mlsanity.com/paper/2605.15071/) (2026)
- [Computational conceptual history of scientific concepts: From early digital methods to LLMs](https://mlsanity.com/paper/2606.04118/) (2026)
- [Where Models Converge and Humans Diverge: A Coverage Framework for Distributional Pluralism in Open-Ended Generation](https://mlsanity.com/paper/2608.05576/) (2026)
- [How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling](https://mlsanity.com/paper/2609.02482/) (2026)
- [Chronologic: Measuring Language Models' Ability to Represent the Past](https://mlsanity.com/paper/2609.23178/) (2026)
- [Who Warmed the Archives? LLMs Overestimate Historical Warmth](https://mlsanity.com/paper/2609.37499/) (2026)
- [L"ushi-1840: Building and Probing a Knowledge Cutoff for a Classical Chinese Language Model](https://mlsanity.com/paper/2610.16201/) (2026)
- [Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction](https://mlsanity.com/paper/2610.19028/) (2026)

## Abstract (arXiv)

Before researchers can use language models to simulate the past, they need to understand the risk of anachronism. We find that prompting a contemporary model with examples of period prose does not produce output consistent with period style. Fine-tuning produces results that are stylistically convincing enough to fool an automated judge, but human evaluators can still distinguish fine-tuned model outputs from authentic historical text. We tentatively conclude that pretraining on period prose may be required in order to reliably simulate historical perspectives for social research.