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20242026
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cs.CL2026

Can Released LLM Vocabularies Support Token-Level Estimation of Hidden Corpora?

Qingjie Zhang, Xingzhang Ren, Zixuan Chen +6

Pretraining corpus composition shapes LLM capabilities, but it often remains hidden even when model weights are released. Prior work has inferred corpus mixtures or traced specific…

cs.CL2026

Auditing Chinese Web-scale Corpora via Sampled BPE Token Statistics

Qingjie Zhang, Ziqi Tang, Jie Zhang +7

Chinese web pollution has surfaced in LLMs, motivating audits of upstream Chinese corpora. However, auditing such corpora faces three challenges: (1) their web-scale size makes ful…

cs.CL2025

Speculating LLMs' Chinese Training Data Pollution from Their Tokens

Qingjie Zhang, Di Wang, Haoting Qian +7

Tokens are basic elements in the datasets for LLM training. It is well-known that many tokens representing Chinese phrases in the vocabulary of GPT (4o/4o-mini/o1/o3/4.5/4.1/o4-min…

cs.CL2025

Understanding the Dilemma of Unlearning for Large Language Models

Qingjie Zhang, Haoting Qian, Zhicong Huang +5

Unlearning seeks to remove specific knowledge from large language models (LLMs), but its effectiveness remains contested. On one side, "forgotten" knowledge can often be recovered…

cs.CL2024

Understanding the Dark Side of LLMs' Intrinsic Self-Correction

Qingjie Zhang, Di Wang, Haoting Qian +7

Intrinsic self-correction was proposed to improve LLMs' responses via feedback prompts solely based on their inherent capability. However, recent works show that LLMs' intrinsic se…