The study examines how the geometric placement of synthetic data affects discourse‑pragmatic function classification. Using 410 annotated instances of the word "look" from the British National Corpus, synthetic examples were generated with Llama 3.1 and grouped by cosine distance from real data in RoBERTa space. Six training conditions were compared, showing that examples close to real data (NEAR) yield the largest macro‑F gain, while a distance‑balanced mix gives the highest accuracy, yet none improve AUC.
By Sara Sorahi, Kevin Tang, Reza Kazemian
arXiv:2604. 21137v3 Announce Type: replace-cross Abstract: Analyzing the reasoning patterns of students in science classrooms is critical for understanding knowledge construction mechanism and improving instructional practice to maximize cognitive engagement, yet manual coding of classroom discourse at scale remains prohibitively labor-intensive.
By Jiho Noh, Mukhesh Raghava Katragadda, Raymond Carl, Soon Lee
The study probes Gemma‑2‑9B‑IT with Sparse Autoencoders across English, Hebrew, and Russian to examine how multilingual LLMs handle informal register. By using a dataset of polysemous terms that appear in literal and informal contexts, the authors isolate pragmatic register processing from lexical cues. They discover a small, robust cross‑linguistic core that forms an informal register subspace, which becomes clearer in deeper layers and can causally shift output formality across all tested languages, even transferring zero‑shot to six unseen languages.
By Uri Z. Kialy, Avi Shtarkberg, Ayal Klein
The paper investigates how adjectival modifiers affect the semantic plausibility of events, using the Adept benchmark of 16,000 English sentence pairs that differ by a single adjective. Experiments show that sentence transformers, despite being conceptually suited to the task, underperform compared to models like RoBERTa. The authors provide an error analysis and discuss the implications of their findings for future work on balancing training and test data.
By Anna Golub, Beate Zywietz, Annerose Eichel
arXiv:2606.18389v2 Announce Type: replace
Abstract: Large language models (LLMs) have become an effective tool for synthetic data generation, including for low-resource languages, where generated dat...
By Jan Cegin, Daniil Gurgurov, Yusser Al Ghussin, Simon Ostermann
arXiv:2510.12306v4 Announce Type: replace
Abstract: As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic...
By Cameron Morin, Matti Marttinen Larsson