most citedWhen Every Token Counts: Optimal Segmentation for Low-Resource Language Models

1 citations · 3 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2025

DiagramIR: An Automatic Pipeline for Educational Math Diagram Evaluation

Vishal Kumar, Shubhra Mishra, Rebecca Hao +3

Large Language Models (LLMs) are increasingly being adopted as tools for learning; however, most tools remain text-only, limiting their usefulness for domains where visualizations…

cs.CL2025

Dialect Normalization using Large Language Models and Morphological Rules

Antonios Dimakis, John Pavlopoulos, Antonios Anastasopoulos

Natural language understanding systems struggle with low-resource languages, including many dialects of high-resource ones. Dialect-to-standard normalization attempts to tackle thi…

cs.CL2025

ZeroSumEval: An Extensible Framework For Scaling LLM Evaluation with Inter-Model Competition

Hisham A. Alyahya, Haidar Khan, Yazeed Alnumay +2

We introduce ZeroSumEval, a dynamic, competition-based, and evolving evaluation framework for Large Language Models (LLMs) that leverages competitive games. ZeroSumEval encompasses…

cs.CL2024★ 1 cited

When Every Token Counts: Optimal Segmentation for Low-Resource Language Models

Bharath Raj, Garvit Suri, Vikrant Dewangan +1

Traditional greedy tokenization methods have been a critical step in Natural Language Processing (NLP), influencing how text is converted into tokens and directly impacting model p…

cs.CL2024

LLäMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch

Jan Pfister, Julia Wunderle, Andreas Hotho

We create two German-only decoder models, LLäMmlein 120M and 1B, transparently from scratch and publish them, along with the training data, for the German NLP research community to…

cs.CL2024★ 1 cited

AmpleGCG-Plus: A Strong Generative Model of Adversarial Suffixes to Jailbreak LLMs with Higher Success Rates in Fewer Attempts

Vishal Kumar, Zeyi Liao, Jaylen Jones +1

Although large language models (LLMs) are typically aligned, they remain vulnerable to jailbreaking through either carefully crafted prompts in natural language or, interestingly,…

cs.CL2024★ 1 cited

Review of Computational Epigraphy

Vishal Kumar

Computational Epigraphy refers to the process of extracting text from stone inscription, transliteration, interpretation, and attribution with the aid of computational methods. Tra…