CHRONOBERG: Capturing Language Evolution and Temporal Awareness in Foundation Models

arXiv:2509.22360 · cs.CL, cs.AI · Submitted 2025-09-26 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "CHRONOBERG: Capturing Language Evolution and Temporal Awareness in Foundation Models".

Jane: The paper was written by Subarnaduti Paul and Niharika Hegde from University of Bremen and DFKI.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Title: Tom: We're kicking things off with 'CHRONOBERG: Capturing Language Evolution and Temporal Awareness in Foundation Models'.

Jane: It's a heavy title, Tom, but the core idea is quite simple and human.

Tom: Are you saying we're talking about how our words change as we do, Jane?

Jane: Yes, because the authors, Niharika Hegde and Subarnaduti Paul, noticed that AI models often treat language as if it's frozen in a single moment.

Tom: That sounds like a massive blind spot for any system that needs to understand us.

Jane: It really is, since words change their meanings and even their emotional weight as decades pass.

Tom: So a model might read a book from one thousand eight hundred and completely miss the point?

Jane: Precisely, it might apply a modern context to a word that meant something totally different back then.

Tom: I can see how that would lead to some pretty strange misunderstandings.

Jane: It's exactly what this research team is trying to fix by giving models a sense of time.

Lu: I find the potential here absolutely massive for creating AI that acts like a true digital historian.

Tom: A historian that doesn't just list dates but actually understands the nuance of the era?

Lu: Exactly, it could grasp the specific social norms and cultural shifts of the 19th century just by reading the vocabulary.

Meng: From a practical standpoint, this helps us deal with concept drift in the real world.

Tom: You mean when the data a model sees in production starts to shift away from what it learned?

Meng: That's right, and understanding these long-term shifts could make our systems far more robust and reliable.

Lalam: It's also about making sure our technology respects the full breadth of human expression across history.

Tom: That's a beautiful way to put it, Lalam.

Jane: We've touched on the why, but now we need to look at the how.

Tom: Let's see how they actually built this Chronoberg dataset.

Summary: Tom: Now that we know why this matters, let's look at how they actually built Chronoberg.

Jane: They didn't just scrape the web; they curated twenty-five thousand sixty-one books from Project Gutenberg.

Tom: That's a huge collection, isn't it?

Jane: It is, covering two hundred fifty years of English text and totaling two point seven billion tokens.

Tom: How did they ensure they weren't just using random dates for these books?

Jane: They used OpenLibrary to verify the publication years, keeping the error margin very low.

Meng: That's a smart move because bad metadata would ruin a temporal study.

Tom: It's a huge engineering lift to clean all that up, isn't it, Meng?

Meng: It definitely is, especially when you're trying to maintain consistency across centuries.

Lu: What really caught my eye was their use of VAD scores.

Tom: You mean the Valence, Arousal, and Dominance measurements?

Lu: Yes, they used those to give every word an emotional profile that changes over time.

Jane: It's like giving the AI an emotional compass that updates every few decades.

Tom: So it can track if a word becomes more positive or negative as time goes on?

Jane: Exactly, it maps the emotional trajectory of the language itself.

Lu: I see it as a way to map the emotional landscape of history.

Lalam: This allows us to see how the collective sentiment of entire eras has shifted.

Tom: It's a much more sophisticated way to look at data than just counting words.

Jane: And it leads directly into their experiments with how models actually handle this information.

Tom: Let's see what happened when they put these models to the test.

Improvements: Tom: We've seen the data, but now let's talk about the actual results of their testing.

Jane: The findings were quite revealing, especially regarding how models handle harmful language.

Tom: They found that modern hate speech detectors actually fail in historical contexts?

Jane: Yes, they struggle because they're looking for modern keywords instead of understanding the actual sentiment.

Tom: Are you thinking of those examples where they misinterpret words like 'hussy' or 'gay'?

Jane: That's a perfect example, as the models apply modern connotations that don't fit the era.

Meng: They also looked at how models learn this new info using EWC and LoRA.

Tom: To see if they could adapt without losing what they already knew?

Meng: That's the goal, but the results showed they still face a massive forgetting problem.

Jane: The models tend to overwrite old meanings when they encounter new ones.

Tom: So the new information just pushes out the historical context?

Jane: In many cases, yes, which is why their perplexity scores go up so much.

Lu: It's a struggle to balance two different meanings for the same word in one model.

Tom: Even with advanced techniques like LoRA, they can't quite solve it?

Lu: Not yet, because the semantic drift is just so volatile and contradictory.

Lalam: This shows that our current training methods aren't yet ready for the reality of time.

Tom: It's a call to action for anyone building foundation models.

Jane: We've covered a lot of ground, so let's wrap this up.

Conclusion: Tom: We're coming to the end of our look at 'CHRONOBERG: Capturing Language Evolution and Temporal Awareness in Foundation Models'.

Jane: It's been a fascinating journey through the history of words.

Tom: This research really exposes how much we need to think about time in AI.

Jane: It moves us away from the idea that language is a static thing.

Lu: I'm excited to see models that can truly bridge the gap between different centuries.

Meng: I'm looking forward to seeing how this makes real-world systems more reliable.

Lalam: It's a vital step toward an AI that understands the full story of human culture.

Tom: Thanks for joining us for this deep dive.

Jane: We'll see you next time!

University of Bremen · DFKI

cs.CL, cs.AI

Submitted: 2025-09-26

Updated: 2026-09-10

Code: https://github.com/paulsubarna/Chronoberg

License: http://creativecommons.org/licenses/by-nc-sa/4.0/

Importance score: 92/100

The gist: CHRONOBERG is a specialized corpus designed to capture "Language Evolution and Temporal Awareness in Foundation Models." This resource is critical for advancing Natural Language Processing research

Key concepts

Temporal Awareness
The core concept is giving AI a sense of time, recognizing that language is not static. It means understanding how word meanings and emotional weight change significantly as decades pass, allowing the model to grasp the nuance of a specific era.
CHRONOBERG Dataset
This dataset was created by curating over twenty-five thousand Project Gutenberg books, covering two hundred fifty years of English text. It uses verified publication years and massive amounts of data to study language changes across historical time periods.
Semantic Drift
This is the technical problem where the meaning or context of a word shifts over long periods. The episode notes that AI models struggle with this, often overwriting old historical meanings when they encounter new information, leading to inaccuracies.
VAD Scores
These measurements (Valence, Arousal, and Dominance) were used by the researchers to give every word an emotional profile. This allows the AI to map the emotional trajectory of language itself and see how collective sentiment shifts over time.

Terminology

Summary

CHRONOBERG is a specialized corpus designed to capture Language Evolution and Temporal Awareness in Foundation Models. This resource is critical for advancing Natural Language Processing research by providing researchers with a mechanism to study how language changes over time, allowing AI models to move beyond static knowledge bases toward systems that can understand the historical context and temporal shifts of human communication. By analyzing this dataset, researchers can develop LLMs capable of continually adapting... to historically evolving concepts.

Dataset Structure and Composition

The corpus spans a defined period, providing texts from Late Modern English. The dataset is made available in two distinct versions to accommodate varied research needs: one version consists of the raw texts grouped by publication year, while the second provides processed texts that are split into annotated sentences and likewise organized by publication year. To ensure reproducibility, a public GitHub repository detailing the entire preprocessing, cleaning, and labelling process is maintained. Furthermore, the dataset is publicly accessible on HuggingFace under a BSD 2-Clause “Simplified” License. The data itself is derived from copyright-free sources and can be viewed as an evergrowing temporal dataset of historical contexts.

Affective Annotation and Metadata

Beyond simple textual organization, CHRONOBERG enriches each sentence with detailed metadata designed to capture the emotional tone of the writing. Specifically, for every sentence, the corpus provides Valence, Arousal, and Dominance (VAD) scores. These scores are intended to capture the affective sentiment expressed in the text. This advanced annotation allows researchers to analyze not only what was said but also how that language was perceived emotionally at different points in time.

Scientific Applications and Downstream Tasks

The primary utility of CHRONOBERG lies in its capacity for sophisticated temporal analysis, enabling several potential downstream applications. These include:

  1. Continually adapting LLMs to historically evolving concepts, allowing models to track semantic drift over decades.

  2. Inspecting words and sentences that have undergone diachronic shifts, which is vital for understanding how meaning changes across time. This capability can inform future prospects, such as unlearning or modifying specific connotations in words used in contemporary English texts.

  3. General training and benchmarking of foundation models on temporally diverse linguistic data.

Ethical Considerations and Usage Limitations

Given the sensitive nature of language analysis, users must exercise extreme caution when utilizing CHRONOBERG. The authors strongly caution against interpreting the VAD scores as definitive labels of positivity or negativity, noting that not every negatively scored sentence is inherently harmful. Therefore, researchers are discouraged from treating the dataset for benchmarking hateful versus non-hateful applications. Furthermore, because the data may directly or indirectly involve individuals, its use to specifically single out any individuals solely based on the VAD lexicons and affective polarity scores of the texts is strongly advised against.

Improvements for AI systems

Improvement 1: Diachronic Sentiment-Alignment Layers for Safety Classifiers

Capability: By integrating the Chronoberg temporally calibrated VAD (Valence-Arousal-Dominance) lexicons into the moderation and hate-speech detection pipeline, the AI can dynamically adjust its sensitivity thresholds based on the detected era of the text. This allows the system to distinguish between modern slurs and historical terms that have since undergone semantic shifts (e.g., faggot or gay), drastically reducing false-positive rates in historical document processing, legal archiving, and digital humanities research.

Improvement 2: Semantic Drift-Aware Continual Learning (SD-CL) Regularization

Capability: Instead of utilizing standard Elastic Weight Consolidation (EWC) which only penalizes parameter changes to prevent forgetting, the AI will implement a regularization term that accounts for the volatility of specific tokens. By using the Chronoberg valence-shift analysis to identify words with high diachronic variance, the model can apply high plasticity to volatile words (allowing it to learn new meanings) while applying high stability to valence-stable words. This results in a model that can undergo long-horizon training without catastrophic forgetting and with significantly improved forward generalization to future linguistic shifts.

Improvement 3: Time-Conditioned Transformer Architectures via Temporal Metadata Injection

Capability: By injecting inferred publication years as continuous temporal embeddings into the transformer architecture during pre-training (modeled after the Chronoberg metadata recovery pipeline), the AI can perform Temporal Semantic Reasoning. The system will be able to condition its latent representations on a specific timestamp, allowing it to accurately interpret the normative meaning of a sentence as it would have been understood in a specific decade, rather than applying a single, stationary, modern-day semantic lens.

Improvement 4: Diachronic Robustness Benchmarking for Concept Drift

Capability: By utilizing the Chronoberg dataset to create a Temporal Volatility Score for every word in a model's vocabulary, developers can proactively identify which parts of a model's knowledge base are most susceptible to concept drift. This allows for the creation of targeted fine-tuning protocols to harden the model against future linguistic evolution, ensuring that the AI remains robust and contextually aware as societal norms and language continue to evolve.

Abstract

Large language models (LLMs) excel at operating at scale by leveraging social media and various data crawled from the web. Whereas existing corpora are diverse, their frequent lack of long-term temporal structure may however limit an LLM's ability to contextualize semantic and normative evolution of language and to capture diachronic variation. To support analysis and training for the latter, we introduce CHRONOBERG, a temporally structured corpus of English book texts spanning 250 years, curated from Project Gutenberg and enriched with a variety of temporal annotations. First, the edited nature of books enables us to quantify lexical semantic change through time-sensitive Valence-Arousal-Dominance (VAD) analysis and to construct historically calibrated affective lexicons to support temporally grounded interpretation. With the lexicons at hand, we demonstrate a need for modern LLM-based tools to better situate their detection of discriminatory language and contextualization of sentiment across various time-periods. In fact, we show how language models trained sequentially on CHRONOBERG struggle to encode diachronic shifts in meaning, emphasizing the need for temporally aware training and evaluation pipelines, and positioning CHRONOBERG as a scalable resource for the study of linguistic change and temporal generalization. Disclaimer: This paper includes language and display of samples that could be offensive to readers. Open Access: Chronoberg is available publicly on HuggingFace at (https://huggingface.co/datasets/spaul25/Chronoberg). Code is available at (https://github.com/paulsubarna/Chronoberg).

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