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Example-Guided Prompting for Document-Level Text Simplification
Marina Litvak, Ariel Perstin, Ilan Shtilman +1
Document-level text simplification requires large language models (LLMs) to rewrite complex documents while preserving meaning, readability, and discourse coherence. Although promp…
A Human-in-the-Loop Corpus for LLM-Based Simplification of Scientific Summaries
Kyuri Im, Michael Färber
Interdisciplinary research is accelerating, yet scientific papers remain difficult to understand outside their home fields. We study large language model (LLM)-based simplification…
NRITYAM: Language Models Meet Art and Heritage of Dance
Punit Kumar Singh, Niladri Ghosh, Advait Joshiınst +3
Language models have become essential tools in shaping modern workflows. However, their global effectiveness hinges on a nuanced understanding of local socio-cultural contexts. To…
Quantifying the Impact of Translation Errors on Multilingual LLM Evaluation
Klaudia-Doris Thellmann, Bernhard Stadler, Michael Färber +1
Machine-translated benchmarks are widely used to assess the multilingual capabilities of large language models (LLMs), yet translation errors in these benchmarks remain underexplor…
Tracing Relational Knowledge Recall in Large Language Models
Nicholas Popovič, Michael Färber
We study how large language models recall relational knowledge during text generation, with a focus on identifying latent representations suitable for relation classification via l…
Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference
Cornelius Kummer, Lena Jurkschat, Michael Färber +1
With the wide adoption of language models for IR -- and specifically RAG systems -- the latency of the underlying LLM becomes a crucial bottleneck, since the long contexts of retri…
Diagnosing Translated Benchmarks: An Automated Quality Assurance Study of the EU20 Benchmark Suite
Klaudia Thellmann, Bernhard Stadler, Michael Färber
Machine-translated benchmark datasets reduce costs and offer scale, but noise, loss of structure, and uneven quality weaken confidence. What matters is not merely whether we can tr…
Semantic Calibration Prevails Where Token Confidence Fails: Benchmarking Long-Form Scientific QA
Philip Müller, Nicholas Popovič, Michael Färber +1
Reliable uncertainty quantification (UQ) is essential for safe deployment of large language models (LLMs) in scientific question answering, where long-form outputs exceed practical…