arXiv (All)
open access
From Outliers to Topics in Language Models: Anticipating Trends in News Corpora
This paper examines how outliers, often dismissed as noise in topic modeling, can act as weak signals of emerging topics in dynamic news corpora. Using vector embeddings from state-of-the-art language models and a cumulative clustering approach, we track their evolution over time in French and Engli
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Bias in Large Language Models: Origin, Evaluation, and Mitigation#189222
- Predicting Emerging Topics from Outliers: A Prospective Study of Weak Signals in Embedding Space#1077112
- Information Theoretic Adversarial Training of Large Language Models#160754
- Unleash LLMs Potential for Sequential Recommendation by Coordinating Dual Dynamic Index Mechanism#1011978
- Competence-Gated Pooling of Language Models and Priors for Event Forecasting#1000072
Record · ID 1036579
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive