arXiv Machine Learning

Right Knowledge, Wrong Answer: Characterizing Parametric Temporal Conflict in Open-Weight Language Models

arXiv:2606. 20959v2 Announce Type: replace Abstract: Language models may encode both outdated facts and their newer replacements.

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

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

Aug 11

Time Present and Time Past: Benchmarking Large Language Models on Temporally Evolving Document Understanding

arXiv:2608. 08512v1 Announce Type: new Abstract: Evolving documents, such as laws, tax codes, and software documentation, are amended, replaced, and sometimes reverted over time, so a question has different correct answers at different dates.

By Mahbub E Sobhani, Md. Faiyaz Abdullah Sayeedi, Fahmid Hasan Chowdhury, Md Adnan Arefeen, Farig Sadeque, Md. Faizul Bari, Swakkhar Shatabda

llms benchmarks

arXiv Machine Learning

Jul 22

A Better Start for Language Models: Domain-Conditional Position Offsets

arXiv:2607. 18302v1 Announce Type: new Abstract: Autoregressive language models are least accurate at the beginning of a sequence, where little context forces reliance on a generic pretraining prior.

By Ye Qiao

llms rag fine-tuning safety

arXiv Machine Learning

Aug 3

RTLCurator: Label-Efficient Data Curation for RTL Generation

arXiv:2607. 29283v1 Announce Type: cross Abstract: Training large language models (LLMs) to write register-transfer level (RTL) requires large corpora of paired specifications and code, and such data is scarce enough that most public corpora are now synthesized.

By Siyang Cai, Cangyuan Li, Wenjing Chang, Kun Wang, Haoyu Gao, Yinhe Han, Ying Wang

llms safety

arXiv Machine Learning

Jun 15

What Drives Test-Time Adaptation for CLIP? A Controlled Empirical Study from an Update Perspective

arXiv:2606. 14299v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) such as CLIP have become a standard backbone for open-vocabulary recognition, yet their zero-shot predictions remain vulnerable to distribution shifts encountered at deployment.

By Jiazhen Huang, Xiao Chen, Zhiming Liu, Yaru Sun, Jingyan Jiang, Zhi Wang

llms multimodal benchmarks

arXiv Machine Learning

1d ago

Do Language Models Consistently Encode the Current Year?

arXiv:2608. 15507v1 Announce Type: cross Abstract: A consistent concept of the current time is important for temporal reasoning, yet how language models represent the current time is not well understood.

By Suze van Adrichem, Aditi Bhaskar, Diyi Yang, Christopher Potts, Jing Huang

llms fine-tuning

arXiv AI

Jun 2

Uncovering Competency Gaps in Large Language Models and Their Benchmarks

arXiv:2512. 20638v2 Announce Type: replace-cross Abstract: The evaluation of large language models relies heavily on standardized benchmarks.

By Maty Bohacek, Nino Scherrer, Nicholas Dufour, Thomas Leung, Christoph Bregler, Stephanie C. Y. Chan

llms benchmarks

arXiv Machine Learning

Aug 5

Speculative Decoding and the Curse of Multilinguality

arXiv:2605. 30580v2 Announce Type: replace-cross Abstract: Speculative decoding is a popular technique for large language model (LLM) inference, enabling faster generation by drafting multiple tokens with a smaller draft model.

By Nirajan Paudel, Michael Ginn, Luc De Nardi, Alexis Palmer

llms efficiency

arXiv AI

Jul 28

Reference Feature Atlases for Mechanistic Auditing of Language Models

arXiv:2607. 22570v1 Announce Type: new Abstract: Auditing a new language model usually means relearning and reinterpreting its internal features from scratch.

By Rui Wu, Tong Che

llms fine-tuning benchmarks

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 Machine Learning

Jun 5

Temporal Preference Concepts and their Functions in a Large Language Model

arXiv:2606. 05194v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly being deployed to make decisions that require trading off near-term gains against long-term consequences, yet little is known about how they internally represent or resolve these tradeoffs.

By Ian Rios-Sialer, Shantanu Darveshi, Shuai Jiang, Avigya Paudel, Anastasiia Pronina, Ipshita Bandyopadhyay, Justin Shenk

llms safety

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