# HistLens: Mapping Idea Change across Concepts and Corpora

arXiv: https://arxiv.org/abs/2604.11749 · HTML: https://mlsanity.com/paper/2604.11749/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 39 references that are in the map.
- First posted on arXiv 13 Apr 2026.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Sparse Autoencoders Find Highly Interpretable Features in Language Models](https://mlsanity.com/paper/2309.08600/) (2023)
- [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://mlsanity.com/paper/1909.00512/) (2019)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [k-Sparse Autoencoders](https://mlsanity.com/paper/1312.5663/) (2013)
- [Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases](https://mlsanity.com/paper/2006.03955/) (2020)
- [All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality](https://mlsanity.com/paper/2109.04404/) (2021)
- [The Dynamic Embedded Topic Model](https://mlsanity.com/paper/1907.05545/) (2019)
- [Disentangling Dense Embeddings with Sparse Autoencoders](https://mlsanity.com/paper/2408.00657/) (2024)
- [LinguaLens: Towards Interpreting Linguistic Mechanisms of Large Language Models via Sparse Auto-Encoder](https://mlsanity.com/paper/2502.20344/) (2025)
- [Evolving linguistic divergence on polarizing social media](https://mlsanity.com/paper/2309.01659/) (2023)
- [textitSwap and Predict -- Predicting the Semantic Changes in Words across Corpora by Context Swapping](https://mlsanity.com/paper/2310.10397/) (2023)
- [A Discourse Analysis Framework for Legislative and Social Media Debates](https://mlsanity.com/paper/2407.06149/) (2024)
- [Word Embeddings Track Social Group Changes Across 70 Years in China](https://mlsanity.com/paper/2504.12327/) (2025)
- [Chronoberg: A 250-Year Diachronic Corpus with Temporal Affective Lexicons for Language Model Analysis](https://mlsanity.com/paper/2509.22360/) (2025)
- [Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses](https://mlsanity.com/paper/2401.13905/) (2024)
- [News Deja Vu: Connecting Past and Present with Semantic Search](https://mlsanity.com/paper/2406.15593/) (2024)
- [Identifying Emerging Concepts in Large Corpora](https://mlsanity.com/paper/2502.21315/) (2025)

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

- [ChronoLens: Measuring Language Change Across Time, Languages, and Linguistic Levels](https://mlsanity.com/paper/2608.03507/) (2026)

## Abstract (arXiv)

Language change both reflects and shapes social processes, and the semantic evolution of foundational concepts provides a measurable trace of historical and social transformation. Despite recent advances in diachronic semantics and discourse analysis, existing computational approaches often (i) concentrate on a single concept or a single corpus, making findings difficult to compare across heterogeneous sources, and (ii) remain confined to surface lexical evidence, offering insufficient computational and interpretive granularity when concepts are expressed implicitly. We propose HistLens, a unified, SAE-based framework for multi-concept, multi-corpus conceptual-history analysis. The framework decomposes concept representations into interpretable features and tracks their activation dynamics over time and across sources, yielding comparable conceptual trajectories within a shared coordinate system. Experiments on long-span press corpora show that HistLens supports cross-concept, cross-corpus computation of patterns of idea evolution and enables implicit concept computation. By bridging conceptual modeling with interpretive needs, HistLens broadens the analytical perspectives and methodological repertoire available to social science and the humanities for diachronic text analysis.