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20022026
most citedAutomatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields

643 citations

Showing cs.CLShow all

10 papers · 1 filter

cs.CL2026

Rethinking Ground Truth: A Case Study on Human Label Variation in MLLM Benchmarking

Tomas Ruiz, Tanalp Agustoslu, Carsten Schwemmer

Human Label Variation (HLV), i.e. systematic differences among annotators' judgments, remains underexplored in benchmarks despite rapid progress in large language model (LLM) devel…

cs.CL2025

Survey Response Generation: Generating Closed-Ended Survey Responses In-Silico with Large Language Models

Georg Ahnert, Anna-Carolina Haensch, Barbara Plank +1

Many in-silico simulations of human survey responses with large language models (LLMs) focus on generating closed-ended survey responses, whereas LLMs are typically trained to gene…

cs.CL2025★ 1 cited

AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation

Leah von der Heyde, Anna-Carolina Haensch, Bernd Weiß +1

The recent development and wider accessibility of LLMs have spurred discussions about how they can be used in survey research, including classifying open-ended survey responses. Du…

cs.CL2024★ 2 cited

CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation

Ingo Ziegler, Abdullatif Köksal, Desmond Elliott +1

Building high-quality datasets for specialized tasks is a time-consuming and resource-intensive process that often requires specialized domain knowledge. We propose Corpus Retrieva…

cs.CL2023★ 40 cited

Applying QNLP to sentiment analysis in finance

Jonas Stein, Ivo Christ, Nicolas Kraus +3

As an application domain where the slightest qualitative improvements can yield immense value, finance is a promising candidate for early quantum advantage. Focusing on the rapidly…

cs.CL2023★ 17 cited

Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages

Ayyoob Imani, Peiqin Lin, Amir Hossein Kargaran +8

The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i.e., making them better for about 100 languages. We instead scale LLMs horizontally: we cr…