The paper proposes a generation‑provenance substrate for synthetic speech research objects, binding source specifications, generated content, waveform, target, fact requirements, quality signals, review lineage, and an immutable manifest identity. It audits this substrate in a private Japanese care‑handoff pipeline, documenting 113 assets and 1.552 hours of synthetic speech with linked audio, transcripts, notes, and fact checklists, while noting selective human evidence and source‑specific gaps. The authors argue that provenance is necessary but not sufficient for behavior attribution, requiring additional frozen training runs and intervention evidence, and they provide a compact provenance contract, audit protocol, and a bounded case study.
"whyItMatters":"The study highlights the need for detailed provenance records to enable reliable auditing and attribution of synthetic data behavior, underscoring limitations in current practices and offering a structured framework for future research."
By Sidi Chang, Peiying Zhu
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:2607. 23804v1 Announce Type: cross Abstract: Context attribution methods for large language models (LLMs) identify which input context contributes to the model response.
By Quoc-Huy Trinh, Lin Zhu, Sebastian Szyller
arXiv:2609.38831v1 Announce Type: new
Abstract: Model-attribution classifiers can often identify which language model produced a text, making model-specific writing patterns a signal of provenance. A...
By Haohan Yuan, Simin Chen, Xi Niu, Hanqing Guo, Depeng Xu, Haopeng Zhang
arXiv:2606. 11127v1 Announce Type: cross Abstract: Synthetic post-training pipelines commonly filter generated samples with reward models or holistic LLM judges, yet two practices remain rarely examined together: whether the filtering signal is grounded in the source evidence that induced each generation, and whether rejected samples can be systematically recovered rather than permanently discarded.
By Soham Bhattacharjee, Karun Sharma, Vinay Kumar Sankarapu, Pratinav Seth
arXiv:2601. 22651v2 Announce Type: replace-cross Abstract: Training-data attribution for vision generative models aims to identify which training data influenced a given output.
By Naoki Murata, Yuhta Takida, Chieh-Hsin Lai, Toshimitsu Uesaka, Bac Nguyen, Stefano Ermon, Yuki Mitsufuji