arXiv AI

A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts

arXiv:2606. 27881v1 Announce Type: cross Abstract: Temporal variation poses a unique challenge for named entity recognition (NER) in historical texts, where entities drift in surface form and salience across time.

arXiv AI

Jun 3

Pretraining Language Models on Historical Text

arXiv:2606. 02991v1 Announce Type: cross Abstract: We introduce TypewriterLM, a 7.

By Xiaoxi Luo, Zachary Shinnick, Niclas Griesshaber, Yixuan Wang, Junchi Yu, Freda Shi, Philip Torr, Yao Lu

llms fine-tuning benchmarks data-engineering

arXiv AI

Jul 14

PRISM Edit: One Vector for All Temporal Answers

arXiv:2607. 11327v1 Announce Type: cross Abstract: Model editing keeps large language models (LLMs) up to date without retraining, but temporal facts expose a limitation of the prevailing locate-and-edit paradigm: an update is not always a replacement.

By Chen Huang (Tsinghua University), Qi Zheng (Tsinghua University), Ruiqin Zheng (ByteDance), Long Zeng (Tsinghua University), Yuantong Xu (ByteDance)

llms benchmarks

arXiv AI

Jul 15

Scaling Point-in-Time Language Models

arXiv:2607. 11889v1 Announce Type: cross Abstract: Large language models trained on unrestricted internet corpora inevitably embed information from the future, introducing lookahead bias that compromises the validity of backtests and causal inference in finance and the social sciences.

By Bryan Kelly, Semyon Malamud, Johannes Schwab, Teng Andrea Xu

llms fine-tuning benchmarks safety

Hugging Face Trending Papers

Jun 24

Overview of HIPE-2026: Person-Place Relation Extraction from Multilingual Historical Texts

Was this person ever at that place, and if so, when? Answering such questions from noisy, multilingual historical documents is the central challenge of HIPE-2026, the third edition of the HIPE evaluation series.

llms computer-vision nlp benchmarks

arXiv Machine Learning

Jun 3

Names Don't Matter: Symbol-Invariant Transformer for Open-Vocabulary Learning

arXiv:2601. 23169v2 Announce Type: replace Abstract: Current neural architectures lack a principled way to handle interchangeable tokens, i.

By \.Ilker I\c{s}{\i}k, Wenchao Li

llms rag

Hugging Face Trending Papers

Aug 6

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting

While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited. Since RAG has proven effective in enhancing the capabilities of large language models by incorporating relevant external information, retrieving similar time series sequences as references might also improve accuracy in time series forecasting tasks.

llms rag benchmarks

arXiv AI

Aug 7

TS-RAG: Retrieval Augmented Generation for Time Series Forecasting

arXiv:2608. 06223v1 Announce Type: new Abstract: While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited.

By Yixiong Xiao, Congxi Xiao, Jingbo Zhou

llms rag benchmarks

arXiv Machine Learning

Jul 8

Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift

arXiv:2607. 05908v1 Announce Type: new Abstract: Real-world data distributions evolve over time, inducing temporal distribution shift that can substantially degrade the reliability of deployed machine learning systems.

By Robin Holzinger (Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA), Riccardo Colletti (Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA)

llms computer-vision nlp

arXiv AI

Jul 3

Introduction to Transformers: an NLP Perspective

arXiv:2311. 17633v2 Announce Type: replace-cross Abstract: Transformers have dominated empirical machine learning models of natural language processing.

By Tong Xiao, Jingbo Zhu

llms nlp

Hugging Face Trending Papers

Jul 30

From Single- to Cross-Document: Benchmarking Multi-Granularity Event Analysis of Large Language Models

Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of documents. While large language models (LLMs) have preliminarily achieved promising performance in part of these tasks individually, their capability in event analysis still lacks comprehensive understanding due to restricted document granularity, task designs, and data source of existing benchmarks.

llms rag benchmarks

arXiv AI

Jun 17

Temporal Preference Optimization for Unsupervised Retrieval

arXiv:2606. 17664v1 Announce Type: cross Abstract: Unsupervised dense retrievers offer scalability by learning semantic similarity from unlabeled documents via contrastive learning, but they struggle to capture the temporal relevance, retrieving semantically related but temporally misaligned documents-an important aspect when a document collection spans multiple time periods (e.

By HyunJin Kim, Jaejun Shim, Young Jin Kim, JinYeong Bak

rag safety

arXiv AI

Jun 10

Instruction Finetuning DeepSeek-R1-8B Model Using LoRA and NEFTune

arXiv:2606. 10392v1 Announce Type: new Abstract: Financial named-entity recognition (NER) is essential for translating unstructured financial reports and news into structured knowledge graphs.

By Wu Yuerong, Mingni Luo

llms rag fine-tuning

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