Guizhen Chen, Weiwen Xu, Hao Zhang, Hou Pong Chan, Chaoqun Liu, Lidong Bing, Deli Zhao, Anh Tuan Luu, Yu Rong. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Large Language Models (LLMs) have demonstrated remarkable performance through supervised fine-tuning or in-context learning using gold labels. However, this paradigm is limited by the availability of gold labels, while in certain scenarios, LLMs may need to perform tasks that are too complex for humans to provide such labels. To tackle this challenge, this study explores whether solely utilizing unlabeled data can elicit strong model capabilities. We propose a new paradigm termed zero-to-strong generalization. We iteratively prompt LLMs to annotate unlabeled data and retain high-quality labels by filtering. Surprisingly, we obverse that this iterative process gradually unlocks LLMs’ potential on downstream tasks. Our experiments on extensive classification and reasoning tasks confirm the effectiveness of our proposed framework. Our analysis indicates that this paradigm is effective for both in-context learning and fine-tuning, and for various model sizes.

Comparative Opinion Quintuple Extraction (COQE) aims to extract all comparative sentiment quintuples from product review text. Each quintuple comprises five elements: subject, object, aspect, opinion and preference. With the rise of Large Language Models (LLMs), existing work primarily focuses on enhancing the performance of COQE task through data augmentation, supervised fine-tuning and instruction tuning. Instead of the above pre-modeling and in-modeling design techniques, we focus on innovation in the post-processing. We introduce a model-unaware adaptive chain-of-feedback (COF) method from the perspective of inference feedback and extraction revision. This method comprises three core modules: dynamic example selection, self-critique and self-revision. By integrating LLMs, COF enables dynamic iterative self-optimization, making it applicable across different baselines. To validate the effectiveness of our approach, we utilize the outputs of two distinct baselines as inputs for COF: frozen parameters few-shot learning and the SOTA supervised fine-tuned model. We evaluate our approach on three benchmarks: Camera, Car and Ele. Experimental results show that, compared to the few-shot learning method, our approach achieves F1 score improvements of 3.51%, 2.65% and 5.28% for exact matching on the respective dataset. Even more impressively, our method further boosts performance, surpassing the current SOTA results, with additional gains of 0.76%, 6.54%, and 2.36% across the three datasets.

Yew Ken Chia, Liying Cheng, Hou Pong Chan, Maojia Song, Chaoqun Liu, Mahani Aljunied, Soujanya Poria, Lidong Bing. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

To mitigate the need for large-scale labeled data and reduce the computational costs for adapting the model to new tasks, this study explores in-context learning (ICL) for aspect term extraction (ATE) with large language models (LLMs). We propose the dynamic sentence type prompting tailored for aspect term extraction tasks. Our hypothesis suggests that LLMs can learn task-specific aspects from demonstrations in ICL.We build upon the exploration of ICL for ATE and introduce a method for constructing explicit and sentence-type based demonstrations. This method shifts from arbitrary example selection to a systematic approach which highlights critical sentence types. We are inspired by human analogical reasoning and introduce the link-of-analogy prompting. This enables LLMs to generalize effectively to new ATE tasks. LLMs can draw analogies to known aspects which improves their performance on unseen categories. Meanwhile, the large language models can refine the extraction performance by iteratively refining the extracted results based on the specified criterion. We conduct experiments using the SemEval benchmark corpora, including R14-16 and L14, focusing on the domains of Restaurant and Laptop. Experiments demonstrate that our approach outperforms existing prompting and few-shot learning methods on token-level ATE datasets.

Chaoqun Liu, Wenxuan Zhang, Yiran Zhao, Anh Tuan Luu, Lidong Bing. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Hierarchical topic modeling aims to discover latent topics from a corpus and organize them into a hierarchy to understand documents with desirable semantic granularity. However, existing work struggles with producing topic hierarchies of low affinity, rationality, and diversity, which hampers document understanding. To overcome these challenges, we in this paper propose Transport Plan and Context-aware Hierarchical Topic Model (TraCo). Instead of early simple topic dependencies, we propose a transport plan dependency method. It constrains dependencies to ensure their sparsity and balance, and also regularizes topic hierarchy building with them. This improves affinity and diversity of hierarchies. We further propose a context-aware disentangled decoder. Rather than previously entangled decoding, it distributes different semantic granularity to topics at different levels by disentangled decoding. This facilitates the rationality of hierarchies. Experiments on benchmark datasets demonstrate that our method surpasses state-of-the-art baselines, effectively improving the affinity, rationality, and diversity of hierarchical topic modeling with better performance on downstream tasks.

Traffic prediction is an indispensable component of urban planning and traffic management. Achieving accurate traffic prediction hinges on the ability to capture the potential spatio-temporal relationships among road sensors. However, the majority of existing works focus on local short-term spatiotemporal correlations, failing to fully consider the interactions of different sensors in the long-term state. In addition, these works do not analyze the influences of anomalous factors, or have insufficient ability to extract personalized features of anomalous factors, which make them ineffectively capture their spatiotemporal influences on traffic prediction. To address the aforementioned issues, We propose a global spatio-temporal fusionbased traffic prediction algorithm that incorporates anomaly awareness. Initially, based on the designed anomaly detection network, we construct an efficient anomalous factors impacting module (AFIM), to evaluate the spatio-temporal impact of unexpected external events on traffic prediction. Furthermore, we propose a multi-scale spatio-temporal feature fusion module (MTSFFL) based on the transformer architecture, to obtain all possible both long and short term correlations among different sensors in a wide-area traffic environment for accurate prediction of traffic flow. Finally, experiments are implemented based on real-scenario public transportation datasets (PEMS04 and PEMS08) to demonstrate that our approach can achieve stateof-the-art performance.

Automatic Modulation Classification (AMC) plays a critical role in cognitive radio services, and latest AMC algorithms still suffers from several challenges, such as low detection accuracy, weak robustness, high dependence on labeled samples and so on in low signal-to-noise environments. To address these problems, we propose an efficient automatic modulation classification algorithm using data and knowledge dual-driven mechanism (TDDC). Initially, we design a vision transformer (ViT)-based model to extract attribute-level knowledge from the raw signal. Besides, we propose a data-driven multi-scale feature extraction model based on skip connection, to effectively capture spatio-temporal correlations of signals.Finally, to validate the effectiveness of our approach, we conduct extensive simulation experiments using the RML2016.10a and RML2016.10b datasets.

Aspect term extraction (ATE) is an important Natural Language Processing task, which aims to extract aspect terms from reviews. Recently, data augmentation has emerged as a reliable approach for relieving data sparsity in the NLP area. For ATE, self-labeling and semi-generation methods have been proposed to implement effective data augmentation. However, they either rely on external data or a pretrained generation model. In this paper, we propose a simple and self-contained augmentation method, which produces new instances for augmentation by context decoupling and infrequent term refilling, without using external data and generation models. We conduct experiments on four benchmark SemEval datasets. The test results show that our method yields substantial improvements, and performs comparably to the state-of-the-art method which uses external data.

Cross-lingual topic models have been prevalent for cross-lingual text analysis by revealing aligned latent topics. However, most existing methods suffer from producing repetitive topics that hinder further analysis and performance decline caused by low-coverage dictionaries. In this paper, we propose the Cross-lingual Topic Modeling with Mutual Information (InfoCTM). Instead of the direct alignment in previous work, we propose a topic alignment with mutual information method. This works as a regularization to properly align topics and prevent degenerate topic representations of words, which mitigates the repetitive topic issue. To address the low-coverage dictionary issue, we further propose a cross-lingual vocabulary linking method that finds more linked cross-lingual words for topic alignment beyond the translations of a given dictionary. Extensive experiments on English, Chinese, and Japanese datasets demonstrate that our method outperforms state-of-the-art baselines, producing more coherent, diverse, and well-aligned topics and showing better transferability for cross-lingual classification tasks.

Traffic prediction is an important component in the development of Intelligent Transportation Systems (ITS). To improve the accuracy of traffic prediction, many existing studies focus on combining recurrent neural networks and graph neural networks and achieve certain effects, but these works still suffer from some limitations on realizing rigorous long-term traffic foreseeing in highly dynamic spatio-temporal environments. To address this issue, we propose an efficient skip link-based traffic prediction algorithm with multiscale feature extraction (SKMSGCN). Specifically, we firstly design an available Spatio-temporal Traffic Feature Extraction module (STFE) based on the established information fusion scheme, to sufficiently obtain long-term traffic feature correlations. In addition, by means of memory networks, we propose a Critical traffic Feature Classification module (CFC), to obtain notable features from traffic sensor nodes with different attributions and further increase traffic prediction accuracy. Moreover, in order to efficiently process the input data of enhanced traffic features from CFC module and achieve the precise traffic foreseeing, we construct a long-term traffic prediction module based on graph convolutional networks and gate recurrent units. The extensive experimental results on two typical benchmark datasets firmly demonstrate the effectiveness of the proposed SKMSGCN in quantitative aspects.