arXiv:2609.25298v1 Announce Type: new
Abstract: Cultural evaluation coverage and robustness in language models are difficult to diagnose because pretraining corpora and cultural benchmarks are rarely...
By Yusser Al Ghussin, Eva Gavaller, Cristina Espa\~na-Bonet, Josef van Genabith, Simon Ostermann
arXiv:2608. 10970v1 Announce Type: cross Abstract: Recent advances in Large Language Models (LLMs) have demonstrated strong capabilities in generating semantically relevant concepts and relations, making them promising tools for taxonomy enrichment.
By Zeinab Ghamlouch, Mehwish Alam
arXiv:2605.27268v2 Announce Type: replace-cross
Abstract: Modern Large Language Models (LLMs) are often criticized for producing repetitive and homogeneous text, despite possessing vast latent vocabu...
By Samer Awad, Javier Conde, Carlos Arriaga, Tairan Fu, Javier Coronado-Bl\'azquez, Pedro Reviriego
Recent advances in Large Language Models (LLMs) have demonstrated strong capabilities in generating semantically relevant concepts and relations, making them promising tools for taxonomy enrichment. However, directly relying on LLM-generated expansions often leads to noisy, redundant, or hierarchically inconsistent structures, limiting their reliability for automated taxonomy expansion.
arXiv:2608.30614v1 Announce Type: new
Abstract: Organizing unstructured feedback text into hierarchical taxonomy is a fundamental challenge in NLP, particularly in domains where feedback arrives at m...
By Sandeep Sricharan Mukku, Albert Aristotle Nanda, Rohit Pyati
TransClean introduces a benchmark for identifying and removing translation noise—unwanted text such as language labels, explanations, or bilingual repetitions—from large language model (LLM) outputs. The authors analyzed 790,000 translations from 12 LLMs across 22 language pairs, cataloguing 12 common noise patterns and creating 9,900 noisy‑clean pairs (8,800 synthetic, 1,100 authentic). They evaluated two extraction methods—a span‑based approach using quality estimation models and an LLM‑prompted method—demonstrating the first systematic framework to assess and improve translation cleanliness.
By Shenbin Qian, Yves Scherrer