# HistoLens: An LLM-Powered Framework for Multi-Layered Analysis of Historical Texts -- A Case Application of Yantie Lun

arXiv: https://arxiv.org/abs/2411.09978 · HTML: https://mlsanity.com/paper/2411.09978/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 15 Nov 2024.

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

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Learning How to Ask: Querying LMs with Mixtures of Soft Prompts](https://mlsanity.com/paper/2104.06599/) (2021)
- [Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective](https://mlsanity.com/paper/2305.15408/) (2023)
- [NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data](https://mlsanity.com/paper/2402.15343/) (2024)
- [Large Language Models for Classical Chinese Poetry Translation: Benchmarking, Evaluating, and Improving](https://mlsanity.com/paper/2408.09945/) (2024)
- [Understanding Literary Texts by LLMs: A Case Study of Ancient Chinese Poetry](https://mlsanity.com/paper/2409.00060/) (2024)

## Built on by (most cited first, 5 of 5)

- [Date Fragments: A Hidden Bottleneck of Tokenization for Temporal Reasoning](https://mlsanity.com/paper/2505.16088/) (2025)
- [QuantMCP: Grounding Large Language Models in Verifiable Financial Reality](https://mlsanity.com/paper/2506.06622/) (2025)
- [A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities](https://mlsanity.com/paper/2507.08425/) (2025)
- [Temporal Referential Consistency: Do LLMs Favor Sequences Over Absolute Time References?](https://mlsanity.com/paper/2510.15513/) (2025)
- [Extending AI for Research to the Humanities: A Multi-Agent Framework for Evidence-Grounded Scholarship](https://mlsanity.com/paper/2605.30947/) (2026)

## Abstract (arXiv)

This paper proposes HistoLens, a multi-layered analysis framework for historical texts based on Large Language Models (LLMs). Using the important Western Han dynasty text "Yantie Lun" as a case study, we demonstrate the framework's potential applications in historical research and education. HistoLens integrates NLP technology (especially LLMs), including named entity recognition, knowledge graph construction, and geographic information visualization. The paper showcases how HistoLens explores Western Han culture in "Yantie Lun" through multi-dimensional, visual, and quantitative methods, focusing particularly on the influence of Confucian and Legalist thoughts on political, economic, military, and ethnic. We also demonstrate how HistoLens constructs a machine teaching scenario using LLMs for explainable analysis, based on a dataset of Confucian and Legalist ideas extracted with LLM assistance. This approach offers novel and diverse perspectives for studying historical texts like "Yantie Lun" and provides new auxiliary tools for history education. The framework aims to equip historians and learners with LLM-assisted tools to facilitate in-depth, multi-layered analysis of historical texts and foster innovation in historical education.