3 papers
cs.CL2025
Can Hallucinations Help? Boosting LLMs for Drug Discovery
Shuzhou Yuan, Zhan Qu, Ashish Yashwanth Kangen +1
Hallucinations in large language models (LLMs), plausible but factually inaccurate text, are often viewed as undesirable. However, recent work suggests that such outputs may hold c…
cs.CL2025
DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations
Nicholas PopoviÄ, Ashish Kangen, Tim Schopf +1
Large, high-quality annotated corpora remain scarce in document-level entity and relation extraction in zero-shot or few-shot settings. In this paper, we present a fully automatic,…
cs.CL2025
LLM in the Loop: Creating the ParaDeHate Dataset for Hate Speech Detoxification
Shuzhou Yuan, Ercong Nie, Lukas Kouba +4
Detoxification, the task of rewriting harmful language into non-toxic text, has become increasingly important amid the growing prevalence of toxic content online. However, high-qua…