Can Language Models Represent the Past without Anachronism?
Not on the map. Why?
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.
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
6 of 21 references.
References · 6
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
- 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 752
- 2024Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearningcited by 504
- 2023S3: Social-network Simulation System with Large Language Model-Empowered Agentscited by 233
- 2024Surveying the Dead Minds: Historical-Psychological Text Analysis with Contextualized Construct Representation (CCR) for Classical Chinesecited by 3
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Citing papers per year: 2025: 1; 2026: 13.
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- 2025Meaning Is Not A Metric: Using LLMs to make cultural context legible at scalecited by 10
- 2026Computational Hermeneutics: Evaluating generative AI as a cultural technologycited by 8
- 2026AI as Entertainmentcited by 2
- 2026A Language Model from 1913: Pretraining on Historical Textcited by 2
- 2026Do Language Models Consistently Encode the Current Year?cited by 1
- 2026Stabilising Generative Models of Attitude Changecited by 0
- 2026On the Cultural Anachronism and Temporal Reasoning in Vision Language Modelscited by 0
- 2026Computational conceptual history of scientific concepts: From early digital methods to LLMscited by 0
- 2026Where Models Converge and Humans Diverge: A Coverage Framework for Distributional Pluralism in Open-Ended Generationcited by 0
- 2026How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytellingcited by 0
- 2026Chronologic: Measuring Language Models' Ability to Represent the Pastcited by 0
- 2026Who Warmed the Archives? LLMs Overestimate Historical Warmthcited by 0
- 2026Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction13 Oct 2026
- 2026L"ushi-1840: Building and Probing a Knowledge Cutoff for a Classical Chinese Language Model12 Oct 2026
- 2026Who Warmed the Archives? LLMs Overestimate Historical Warmth27 Sep 2026
- 2026Chronologic: Measuring Language Models' Ability to Represent the Past19 Sep 2026
- 2026How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling2 Sep 2026
- 2026Do Language Models Consistently Encode the Current Year?16 Aug 2026
- 2026Where Models Converge and Humans Diverge: A Coverage Framework for Distributional Pluralism in Open-Ended Generation6 Aug 2026
- 2026Computational conceptual history of scientific concepts: From early digital methods to LLMs2 Jun 2026
- 2026A Language Model from 1913: Pretraining on Historical Text2 Jun 2026
- 2026On the Cultural Anachronism and Temporal Reasoning in Vision Language Models14 May 2026
- 2026Stabilising Generative Models of Attitude Change2 Apr 2026
- 2026Computational Hermeneutics: Evaluating generative AI as a cultural technology31 Mar 2026
2 more
- 2026AI as Entertainment13 Jan 2026
- 2025Meaning Is Not A Metric: Using LLMs to make cultural context legible at scale23 May 2025
- 2025Meaning Is Not A Metric: Using LLMs to make cultural context legible at scalecited by 10
- 2026Computational Hermeneutics: Evaluating generative AI as a cultural technologycited by 8
- 2026AI as Entertainmentcited by 2
- 2026A Language Model from 1913: Pretraining on Historical Textcited by 2
- 2026Stabilising Generative Models of Attitude Changecited by 0
- 2026Computational conceptual history of scientific concepts: From early digital methods to LLMscited by 0
- 2026Where Models Converge and Humans Diverge: A Coverage Framework for Distributional Pluralism in Open-Ended Generationcited by 0
- 2026How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytellingcited by 0
- 2026Chronologic: Measuring Language Models' Ability to Represent the Pastcited by 0
- 2026Who Warmed the Archives? LLMs Overestimate Historical Warmthcited by 0
- 2026L"ushi-1840: Building and Probing a Knowledge Cutoff for a Classical Chinese Language Modelcited by 0
- 2026Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fictioncited by 0
- 2026Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction13 Oct 2026
- 2026L"ushi-1840: Building and Probing a Knowledge Cutoff for a Classical Chinese Language Model12 Oct 2026
- 2026Who Warmed the Archives? LLMs Overestimate Historical Warmth27 Sep 2026
- 2026Chronologic: Measuring Language Models' Ability to Represent the Past19 Sep 2026
- 2026How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling2 Sep 2026
- 2026Where Models Converge and Humans Diverge: A Coverage Framework for Distributional Pluralism in Open-Ended Generation6 Aug 2026
- 2026Computational conceptual history of scientific concepts: From early digital methods to LLMs2 Jun 2026
- 2026A Language Model from 1913: Pretraining on Historical Text2 Jun 2026
- 2026Stabilising Generative Models of Attitude Change2 Apr 2026
- 2026Computational Hermeneutics: Evaluating generative AI as a cultural technology31 Mar 2026
- 2026AI as Entertainment13 Jan 2026
- 2025Meaning Is Not A Metric: Using LLMs to make cultural context legible at scale23 May 2025