The paper presents PersianAnonymizer, a method for anonymizing Persian customer chats by training a compact NER model using supervision from large language models (LLMs). Three instruction‑tuned LLMs—DeepSeek‑V3‑0324, GPT‑OSS‑120B, and Qwen3‑235B‑A22B‑Instruct‑2507—were used to generate span annotations, producing four corpora. A MatinaRoberta token‑classifier trained on each corpus achieved high macro‑F1 and Label Coverage Recall, with the OSS_ZeroShot‑derived NER labeling a 40K‑message test set in about two minutes on a single RTX 3090, demonstrating a practical, low‑cost approach to Persian data anonymization.
By Mohammad Hossein Shalchian, Mostafa Amiri, Amir Mahdi Sadeghzadeh
arXiv:2608. 19957v1 Announce Type: new Abstract: Natural language code retrieval is a rapidly evolving task in computer science.
By Konstantin Chesnokov, Chingiz Mingazov
arXiv:2606. 05781v1 Announce Type: new Abstract: Deploying frontier large language models (LLMs) for domain-specific structured evaluation tasks often incurs substantial latency, cost, and data privacy overhead.
By Srinivasan Manoharan, Dilipkumar Nallusamy, Sachin Kumar, Haifeng Wu
arXiv:2608. 02616v2 Announce Type: replace-cross Abstract: We present what is, to our knowledge, the first systematic evaluation of OpenAI's Privacy Filter (OPF), a 1.
By Rohith Uppala
Manacá-1B is a 1.72‑billion‑parameter, open decoder‑only language model trained from scratch for Brazilian Portuguese, released with a fully containerized, reproducible training pipeline and complete logs. The authors evaluate it against nine open baselines on four Portuguese benchmarks, reporting standard errors and paired significance tests, and find that Manacá-1B outperforms smaller models on LAMBADA‑PT while remaining competitive on commonsense completion. They also uncover a tokenizer‑related evaluation pitfall that can drastically lower accuracy and provide a simple fix, releasing all code, logs, and corrected tokenizer for full reproducibility.
By Bruno Leonardo Santos Menezes, Carlos Leonardo Souza Cardoso, Fabio Andre Machado Porto
arXiv:2607. 02079v1 Announce Type: cross Abstract: We present HaloGuard 1.
By Navaneeth Sangameswaran, Preetham S, Ashmiya Lenin
arXiv:2608. 02616v1 Announce Type: cross Abstract: We present the first independent, systematic evaluation of OpenAI's Privacy Filter (OPF), a 1.
By Rohith Uppala
arXiv:2607. 28862v1 Announce Type: cross Abstract: The rapid development of Large Language Models (LLMs) has led to significant advances across a wide range of language tasks, while simultaneously raising growing concerns about unauthorized data exploitation and privacy leakage.
By Chengshuai Zhao, Pingchuan Ma, Dawei Li, Bohan Jiang, Zhiyuan Yu, Zhen Tan, Huan Liu
The paper systematically studies how anonymizing input data affects large language models (LLMs). Five prominent LLMs were evaluated on eleven benchmarks, comparing performance on original versus pseudonymized inputs. Results show that anonymization generally degrades performance, with larger drops for more capable models and task-dependent effects; reversible anonymization preserves entity uniqueness better than irreversible redaction, and prompting about anonymization offers no benefit.
By Tobias Deu{\ss}er, Max Hahnb\"uck, Lorenz Sparrenberg, Tobias Uelwer, Christian Bauckhage, Rafet Sifa
arXiv:2604. 13899v3 Announce Type: replace-cross Abstract: Instruction-tuned LLMs can annotate thousands of instances at low cost.
By Ahmad Dawar Hakimi, Lea Hirlimann, Isabelle Augenstein, Hinrich Sch\"utze
arXiv:2508.11857v3 Announce Type: replace-cross
Abstract: Tokenization remains a persistent bottleneck in language modeling, especially when vocabulary learning is limited by whitespace boundaries. W...
By Andrei-Valentin T\u{a}nase, Elena Pelican
Large language models (LLMs) achieve strong relation extraction (RE), but their computational demands and reliance on proprietary APIs limit deployment in resource-constrained or privacy-sensitive settings. We investigate how far small language models (SLMs) can close this gap across general-domain and literary text.