Advanced large language models (LLMs) can generate text almost indistinguishable from human-written text, highlighting the importance of LLM-generated text detection. However, current zero-shot techniques face challenges as white-box methods are restricted to use weaker open-source LLMs, and black-box methods are limited by partial observation from stronger proprietary LLMs. It seems impossible to enable white-box methods to use proprietary models because API-level access to the models neither provides full predictive distributions nor inner embeddings. To traverse the divide, we propose **Glimpse**, a probability distribution estimation approach, predicting the full distributions from partial observations. Despite the simplicity of Glimpse, we successfully extend white-box methods like Entropy, Rank, Log-Rank, and Fast-DetectGPT to latest proprietary models. Experiments show that Glimpse with Fast-DetectGPT and GPT-3.5 achieves an average AUROC of about 0.95 in five latest source models, improving the score by 51\% relative to the remaining space of the open source baseline. It demonstrates that the latest LLMs can effectively detect their own outputs, suggesting that advanced LLMs may be the best shield against themselves. We release our code and data at https://github.com/baoguangsheng/glimpse.

Large language models (LLMs) have shown the ability to produce fluent and cogent content, presenting both productivity opportunities and societal risks. To build trustworthy AI systems, it is imperative to distinguish between machine-generated and human-authored content. The leading zero-shot detector, DetectGPT, showcases commendable performance but is marred by its intensive computational costs. In this paper, we introduce the concept of **conditional probability curvature** to elucidate discrepancies in word choices between LLMs and humans within a given context. Utilizing this curvature as a foundational metric, we present **Fast-DetectGPT**, an optimized zero-shot detector, which substitutes DetectGPT's perturbation step with a more efficient sampling step. Our evaluations on various datasets, source models, and test conditions indicate that Fast-DetectGPT not only surpasses DetectGPT by a relative around 75\% in both the white-box and black-box settings but also accelerates the detection process by a factor of 340, as detailed in Table 1.

Ruisheng Cao, Lu Chen, Zhi Chen, Yanbin Zhao, Su Zhu, Kai Yu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Zhi Chen, Lu Chen, Yanbin Zhao, Ruisheng Cao, Zihan Xu, Su Zhu, Kai Yu. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Text simplification (TS) rephrases long sentences into simplified variants while preserving inherent semantics. Traditional sequence-to-sequence models heavily rely on the quantity and quality of parallel sentences, which limits their applicability in different languages and domains. This work investigates how to leverage large amounts of unpaired corpora in TS task. We adopt the back-translation architecture in unsupervised machine translation (NMT), including denoising autoencoders for language modeling and automatic generation of parallel data by iterative back-translation. However, it is non-trivial to generate appropriate complex-simple pair if we directly treat the set of simple and complex corpora as two different languages, since the two types of sentences are quite similar and it is hard for the model to capture the characteristics in different types of sentences. To tackle this problem, we propose asymmetric denoising methods for sentences with separate complexity. When modeling simple and complex sentences with autoencoders, we introduce different types of noise into the training process. Such a method can significantly improve the simplification performance. Our model can be trained in both unsupervised and semi-supervised manner. Automatic and human evaluations show that our unsupervised model outperforms the previous systems, and with limited supervision, our model can perform competitively with multiple state-of-the-art simplification systems.

One daunting problem for semantic parsing is the scarcity of annotation.Aiming to reduce nontrivial human labor, we propose a two-stage semantic parsing framework, where the first stage utilizes an unsupervised paraphrase model to convert an unlabeled natural language utterance into the canonical utterance.The downstream naive semantic parser accepts the intermediate output and returns the target logical form.Furthermore, the entire training process is split into two phases: pre-training and cycle learning.Three tailored self-supervised tasks are introduced throughout training to activate the unsupervised paraphrase model.Experimental results on benchmarks OVERNIGHT and GE-OGRANNO demonstrate that our framework is effective and compatible with supervised training.

Chinese short text matching usually employs word sequences rather than character sequences to get better performance.However, Chinese word segmentation can be erroneous, ambiguous or inconsistent, which consequently hurts the final matching performance.To address this problem, we propose neural graph matching networks, a novel sentence matching framework capable of dealing with multi-granular input information.Instead of a character sequence or a single word sequence, paired word lattices formed from multiple word segmentation hypotheses are used as input and the model learns a graph representation according to an attentive graph matching mechanism.Experiments on two Chinese datasets show that our models outperform the state-of-the-art short text matching models.

Efficient structure encoding for graphs with labeled edges is an important yet challenging point in many graph-based models.This work focuses on AMR-to-text generation -A graph-to-sequence task aiming to recover natural language from Abstract Meaning Representations (AMR).Existing graph-to-sequence approaches generally utilize graph neural networks as their encoders, which have two limitations: 1) The message propagation process in AMR graphs is only guided by the firstorder adjacency information.2) The relationships between labeled edges are not fully considered.In this work, we propose a novel graph encoding framework which can effectively explore the edge relations.We also adopt graph attention networks with higherorder neighborhood information to encode the rich structure in AMR graphs.Experiment results show that our approach obtains new state-of-the-art performance on English AMR benchmark datasets.The ablation analyses also demonstrate that both edge relations and higher-order information are beneficial to graph-to-sequence modeling.

In optical satellite network, the possible optical inter-satellite links (OISLs) candidate for each satellite are usually more than laser communication terminals (LCTs) equipped. This poses the link assignment problem (LAP) of how to assign LCTs of each satellite to establish OISLs with the others. In order to make full use of huge bandwidth provided by LCT, the problem of assigning all satellite LCTs to establish OISLs is modeled as a perfect matching problem in the perfect match model, where a perfect matching is searched over a mixed complete bipartite graph. The regular, random greedy and perfect match model based link assignment schemes are considered. Simulation results show that the average node-to-node distance and LCT utilization of perfect match model based link assignment scheme are better than the regular and random greedy link assignment schemes.