Challenge: Currently, there is no publicly available corpus for diachronic text analysis due to the lack of accurate temporal metadata.
Approach: They propose to add missing temporal metadata to the Gutenberg corpus by using open web, Wikipedia, and Open Library API sources.
Outcome: The proposed corpus includes 53,774 books with a total of 3.8 billion tokens in 11 languages, produced between 1600 and 2000.

Similar Papers

Remember This Event That Year? Assessing Temporal Information and Understanding in Large Language Models (2024.findings-emnlp)

Copied to clipboard

Challenge: Large Language Models (LLMs) are increasingly ubiquitous, yet their ability to effectively retain and reason about temporal information remains limited.
Approach: They propose six metrics to assess three learning paradigms to enhance temporal knowledge acquisition.
Outcome: The proposed methods improve performance and reduce incorrect outputs.
From FreEM to D’AlemBERT: a Large Corpus and a Language Model for Early Modern French (2022.lrec-1)

Copied to clipboard

Challenge: Anguage models for historical states of language are becoming more complex to process and more scarce in the corpora available.
Approach: They propose to use a contextualised language model to analyse historical states of language in French.
Outcome: The proposed model is based on a corpus of historical texts and is evaluated with an NLP task.
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)

Copied to clipboard

Challenge: Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation.
Approach: They propose a generic workflow for LLM-driven synthetic data generation.
Outcome: The proposed workflows highlight gaps in existing research and outline avenues for future studies.
When Benchmarks Age: Temporal Misalignment through Large Language Model Factuality Evaluation (2026.eacl-short)

Copied to clipboard

Challenge: Existing studies on LLM factuality evaluation have not investigated the reliability of static evaluation benchmarks.
Approach: They examine five popular factuality benchmarks and eight LLMs released over different years to assess their reliability.
Outcome: The proposed method compared five popular factuality benchmarks and eight LLMs released over different years.
Entity Cloze By Date: What LMs Know About Unseen Entities (2022.findings-naacl)

Copied to clipboard

Challenge: Existing literature provides benchmarks to measure LMs' knowledge about entities .
Approach: They propose a framework to analyze what language models can infer about new entities that did not exist when they were pretrained.
Outcome: The proposed framework shows that models more informed about the entities achieve lower perplexity on this benchmark.
Temporal Referential Consistency: Do LLMs Favor Sequences Over Absolute Time References? (2025.emnlp-main)

Copied to clipboard

Challenge: Existing efforts to ensure temporal consistency in large language models are lacking in time-sensitive fields . temporal reasoning is essential for time- sensitive fields such as finance and healthcare . a new benchmark aims to improve temporal referent consistency of LLMs .
Approach: They propose a temporal referential consistency benchmark with a resource TEMP-ReCon to assess LLMs across temporal references.
Outcome: The proposed model improves LLMs' temporal consistency by comparing them to baseline models.
Static Models, Dynamic World: A Unified Perspective on Temporal Perception in Large Language Models (2026.findings-acl)

Copied to clipboard

Challenge: Large language models are trained on static corpora but deployed in a dynamic world . a foundational tension remains between time and the ability to understand it .
Approach: They formalize temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers.
Outcome: The proposed framework formalizes temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers . the framework induces four temporal information regimes corresponding to internal reasoning, answer recency, premise anchoring, and genuine world indeterminacy .
TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models (2022.emnlp-main)

Copied to clipboard

Challenge: Language Models (LMs) become outdated as the world changes, a phenomenon called temporal misalignment.
Approach: They propose a lifelong benchmark that utilizes the difference between consecutive snapshots of English Wikipedia and English Wikidata for training and evaluation.
Outcome: The proposed benchmark can be trained on the difference between consecutive snapshots of English Wikipedia and English Wikidata for training and evaluation.
The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)

Copied to clipboard

Challenge: Until recently, language descriptions were available in paper form only, with indexes as the only search aid.
Approach: They propose to digitize a multilingual corpus of language descriptions and annotate it with various meta, word, and text attributes to make searching and analysis easier and more useful.
Outcome: The proposed corpus is searchable through a couple of well-established corpus infrastructures.
WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse (D18-1)

Copied to clipboard

Challenge: a corpus of 43 million atomic edits is available for Wikipedia edit history . edits are instances in which a human editor has inserted a single contiguous phrase into, or deleted a contigous phrase from, an existing sentence.
Approach: They use Wikipedia edit history to mine atomic edits across 8 languages . they find edits contain instances in which a human editor has inserted a single phrase into, or deleted a contiguous phrase from, an existing sentence.
Outcome: The data show that edits differ from the language observed in standard corpora and that models trained on edits encode different aspects of semantics and discourse than models trained in raw text.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations