A classical problem in continuous sparse Fourier transforms, which dates back to Prony's work in 1795, is to learn a $k$-Fourier-sparse signal $x(t):=\sum_{j=1}^k α_j e^{2 π\mathbf{i} f_j t}$ with arbitrary $k$ frequencies $f_1,\ldots,f_k \in \mathbb{R}$. We consider this learning problem within a fixed time window $t \in [-1,1]$ in the presence of bounded $L_2$ adversarial noise. Because the frequencies $f_1,\ldots,f_k$ can be arbitrarily close, accurately recovering individual frequencies is information-theoretically impossible even under exponentially small noise from Moitra (STOC 2015). This work therefore examines two fundamental questions: If an algorithm uses $\tilde{O}(k)$ frequencies to approximate $f_1,\ldots,f_k$ of $x$, what is the optimal covering error? And how can we efficiently learn the continuous signal $x(t)$ as a whole over the time window? Our main result is an algorithm running in time $\tilde{O}(k)$ that outputs a list $L$ of $\tilde{O}(k)$ frequencies with covering error $O(k)$ --- meaning that for most frequencies $f_j$ of $x$, $L$ guarantees $\min_{\tilde{f} \in L} |f_j-\tilde{f}| = O(k)$ --- and an reconstruction $\tilde{x}(t) \approx x(t)$ over $[-1,1]$. When constrained to outputting $\tilde{O}(k)$ frequencies, this $O(k)$ covering error is nearly optimal: Chen and Price (ICALP 2019) demonstrated two indistinguishable $k$-Fourier-sparse signals separated by distance $\tildeΩ(k^2)$ in the frequency domain, which implies an amortized lower bound of $\tildeΩ(k)$ per frequency. Furthermore, our sample and time complexities are nearly optimal, dramatically improving upon the previous best algorithm by Song, Sun, Weinstein, and Zhang (FOCS 2023), which required $\tilde{O}(k^4)$ samples and $\tilde{O}(k^{4ω})$ running time.
Wei Shao Institute of Computing Technology, Chinese Academy of Sciences, Yihang Wang ByteDance Inc. and University of Chinese Academy of Sciences, Gao yu Zhu, Ziqiang Cheng, Lei Yu, Jiafeng Guo Institute of Computing Technolgy, Chinese Academy of Sciences, Xueqi Cheng Institute of Computing Technology, Chinese Academy
Existing detoxification methods for large language models mainly focus on post-training stage or inference time, while few tackle the source of toxicity, namely, the dataset itself. Such training-based or controllable decoding approaches cannot completely suppress the model’s inherent toxicity, whereas detoxifying the pretraining dataset can fundamentally reduce the toxicity that the model learns during training. Hence, we attempt to detoxify directly on raw corpora with SoCD (Soft Contrastive Decoding), which guides an LLM to localize and rewrite toxic spans in raw data while preserving semantics, in our proposed HSPD (Hierarchical Semantic-Preserving Detoxification) pipeline, yielding a detoxified corpus that can drop-in replace the original for fine-tuning or other training. On GPT2-XL, HSPD attains state-of-the-art detoxification, reducing Toxicity Probability (TP) from 0.42 to 0.18 and Expected Maximum Toxicity (EMT) from 0.43 to 0.20. We further validate consistent best-in-class results on LLaMA2-7B, OPT-6.7B, and Falcon-7B. These findings show that semantics-preserving, corpus-level rewriting with HSPD effectively suppresses downstream toxicity while retaining data utility and allowing seamless source-level mitigation, thereby reducing the cost of later model behavior adjustment.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Long context inference scenarios have become increasingly important for large language models, yet they introduce significant computational latency. While prior research has optimized long-sequence inference through operators, model architectures, and system frameworks, tokenization remains an overlooked bottleneck. Existing parallel tokenization methods accelerate processing through text segmentation and multi-process tokenization, but they suffer from inconsistent results due to boundary artifacts that occur after merging. To address this, we propose LoPT, a novel Lossless Parallel Tokenization framework that ensures output identical to standard sequential tokenization. Our approach employs character-position-based matching and dynamic chunk length adjustment to align and merge tokenized segments accurately. Extensive experiments across diverse long-text datasets demonstrate that LoPT achieves significant speedup while guaranteeing lossless tokenization. We also provide theoretical proof of consistency and comprehensive analytical studies to validate the robustness of our method.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表