arXiv:2609.09696v1 Announce Type: new
Abstract: Large language models are increasingly proposed as automated auditors of document quality, yet their reliability as detectors of planted errors is poor...
By Karan Parekh, Sanjana Pendyala Ravinder, Sana Mhapsekar, Medina Maloku
arXiv:2608. 08512v1 Announce Type: new Abstract: Evolving documents, such as laws, tax codes, and software documentation, are amended, replaced, and sometimes reverted over time, so a question has different correct answers at different dates.
By Mahbub E Sobhani, Md. Faiyaz Abdullah Sayeedi, Fahmid Hasan Chowdhury, Md Adnan Arefeen, Farig Sadeque, Md. Faizul Bari, Swakkhar Shatabda
The paper introduces SynthSentry, a model‑agnostic method for detecting synthetic data contamination in language‑model training corpora. It computes a distributional divergence score based on lexical diversity collapse, n‑gram tail truncation, and perplexity variance across reference models, requiring no access to the generating model or synthetic labels. Experiments on English corpora contaminated by small open‑weight generators and an instruction‑tuned model show that SynthSentry ranks contamination severity accurately, maintains low false‑positive rates after calibration, and does not degrade downstream fine‑tuning performance at the tested scale.
By Praveen Kumar Myakala, Ravichandra Namburi, Sowmya Keragodu Jayaramu, Sooraj George Thomas
arXiv:2606. 27383v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as research assistants, yet it remains unclear whether they can calibrate research takeaways to the strength and scope of the supporting evidence.
By Yu Fu, Yongqi Kang, Yong Zhao
arXiv:2608. 11415v1 Announce Type: cross Abstract: Large language models are being proposed as agents in scientific workflows, in domains where no downstream verifier exists.
By Valentin Rodionov, Shamil Assylbekov
arXiv:2607. 29539v1 Announce Type: cross Abstract: Standard AI-text detection benchmarks compare human-written text against text generated directly by large language models (LLMs).
By Gaetano Perrone, Simon Pietro Romano