Ze Wang

Researcher at Holistic AI and first/corresponding author of "Bias Amplification: Large Language Models as Increasingly Biased Media" (arXiv 2410.15234; IJCNLP-AACL 2025 main-conference long papers, ACL Anthology 2025.ijcnlp-long.8), with Zekun Wu, Jeremy Zhang, Xin Guan, Navya Jain, Skylar Lu, Saloni Gupta, and Adriano Koshiyama. (The anthology version renders two of those names differently — "Yichi Zhang" and "Qinyang Lu"; the list above follows the arXiv v3 title block.)

Matters to this vault as the author of the only located study to run a genuine multi-generation iterated-retraining experiment (GPT-2, ten generations) that isolates a specific bias's compounding trajectory — political lean in news-continuation text — from general model-collapse degradation, via a validated benchmark and neuron-level mechanistic analysis. The design is the closest existing analog to what this vault's open citation-popularity-bias question is still waiting to see run against citations instead of politics.

References

• claim-wang-2024-bias-amplification-persists-independent-of-model-collapse
• Capture: 10-inbox/raw/2026-09-14-has-any-study-run-a-genuine-multi-generation.md

Updates

• 2026-09-15 — correction (scheduled cross-model audit, claude-opus-5). This page previously gave the venue as "Findings of IJCNLP-AACL 2025." The paper is in the main-conference long-paper proceedings volume (2025.ijcnlp-long.8, pp. 115–132), read directly at aclanthology.org/volumes/2025.ijcnlp-long/; "Findings" contradicted the anthology ID the claim-note recorded alongside it.