Comprehension of ancient texts plays an important role in archaeology and understanding of Chinese history and civilization. The rapid development of large language models needs benchmarks that can evaluate their comprehension of ancient characters. Existing Chinese benchmarks are mostly targeted at modern Chinese and transmitted documents in ancient Chinese, but the part of excavated documents in ancient Chinese is not covered. To meet this need, we propose the AncientBench, which aims to evaluate the comprehension of ancient characters, especially in the scenario of excavated documents. The AncientBench is divided into four dimensions, which correspond to the four competencies of ancient character comprehension: glyph comprehension, pronunciation comprehension, meaning comprehension, and contextual comprehension. The benchmark also contains ten tasks, including radical, phonetic radical, homophone, cloze, translation, and more, providing a comprehensive framework for evaluation. We convened archaeological researchers to conduct experimental evaluations, proposed an ancient model as baseline, and conducted extensive experiments on the currently best-performing large language models. The experimental results reveal the great potential of large language models in ancient textual scenarios as well as the gap with humans. Our research aims to promote the development and application of large language models in the field of archaeology and ancient Chinese language.

In recent years, rapid advances in Multimodal Large Language Models (MLLMs) have increasingly stimulated research on ancient Chinese scripts.As the evolution of written characters constitutes a fundamental pathway for understanding cultural transformation and historical continuity, how MLLMs can be systematically leveraged to support and advance text evolution analysis remains an open and largely underexplored problem.To bridge this gap, we construct a comprehensive benchmark comprising 11 tasks and over 130,000 instances, specifically designed to evaluate the capability of MLLMs in analyzing the evolution of ancient Chinese scripts.We conduct extensive evaluations across multiple widely used MLLMs and observe that, while existing models demonstrate a limited ability in glyph-level comparison, their performance on core tasks-such as character recognition and evolutionary reasoning-remains substantially constrained.Motivated by these findings, we propose a glyph-driven fine-tuning framework (GEVO) that explicitly encourages models to capture evolutionary consistency in glyph transformations and enhances their understanding of text evolution.Experimental results show that even models at the 2B scale achieve consistent and comprehensive performance improvements across all evaluated tasks.To facilitate future research, we publicly release both the benchmark and the trained models 1 .

Although studies have demonstrated that Large Language Models (LLMs) can perform well on Out-of-Distribution (OOD) tasks, their advantage tends to diminish as the distribution shift becomes more severe. Consequently, researchers aim to retrieve distributionally similar and informative demonstrations from the available source domain to boost the inference capabilities of LLMs. However, in practical scenarios where the target domain is inaccessible, evaluating the unknown distribution is challenging, which indirectly impacts the quality of the selected demonstrations. To address this problem, we propose DOPA, a demonstration search framework that incorporates an OOD proxy to approximate the inaccessible target domain and guide the retrieval process. Building on proxy-based evaluation, DOPA further introduces a Mahalanobis distance-based global diversity constraint to ensure sufficient diversity among the retrieved demonstrations. Experimental results on multiple LLMs and tasks demonstrate that DOPA effectively enhances robustness in OOD settings.

Negotiation agents need to influence the attitudes or intentions of users to reach a consensus. Strategy planning and expressive optimization are crucial aspects of effective negotiations. However, previous studies have typically focused on only one of these aspects, neglecting the fact that their combined synergistic effect can lead to better performance. Inspired by the dual-process theory in human cognition, we propose a Dual-Mind Negotiation Agent (DMNA) framework. This framework integrates an intuitive module for rapid, experience-based response and a deliberative module for slow, expression optimization. The intuitive module is trained using Monte Carlo Tree Search (MCTS) and Direct Preference Optimization (DPO), enabling it to make suitable strategic planning and expression. The deliberative module employs a multifaceted reflexion mechanism to enhance the quality of expression. Experiments conducted on negotiation datasets confirm that DMNA achieves state-of-the-art results, demonstrating an enhancement in the negotiation ability of agents.

Multimodal sarcasm detection (MSD) leverages multimodal data, including both images and text, to detect whether the input content contains sarcastic information. Despite recent advances, existing MSD approaches often overlook the imbalance in sarcastic content between text and image modalities, where text typically carries more sarcastic cues conveyed through complex semantic relationships. To address this issue, we propose a dependency graph-enhanced joint attention network that integrates the flat representations extracted from the joint attention mechanisms and the graph-based representations learned from the dependency graph. Specifically, we design a joint attention module to capture the rich sarcastic cues and semantic relationships within the text, and use the graph-based representations to enhance the flat representations. Evaluated on the HFM dataset, our method achieved 0.54% improvement in F1 score and 0.81% in accuracy, demonstrating its effectiveness.

Few-Shot Relation Classification (FSRC) aims to predict novel relationships by learning from limited samples. Graph Neural Network (GNN) approaches for FSRC constructs data as graphs, effectively capturing sample features through graph representation learning . However, they often face several challenges: 1) They tend to neglect the interactions between samples from different support sets and overlook the implicit noise in labels, leading to sub-optimal sample feature generation. 2) They struggle to deeply mine the diverse semantic information present in FSRC data . 3) Over-smoothing and overfitting limit the model's depth and adversely affect overall performance. To address these issues, we propose a Sample Representation Enhancement model based on Heterogeneous Graph Neural Network (SRE-HGNN) for FSRC. This method leverages inter-sample and inter-class associations (i.e., label mutual attention) to effectively fuse features and generate more expressive sample representations. Edge-heterogeneous GNNs are employed to enhance sample features by capturing heterogeneous information of varying depths through different edge attentions. Additionally, we introduce an attention-based neighbor node culling method, enabling the model to stack higher levels and extract deeper inter-sample associations, thereby improving performance. Finally, experiments are conducted for the FSRC task , and SRE-HGNN achieves an average accuracy improvement of 1.84% and 1.02% across two public datasets.

Hierarchical Text Classification (HTC) organizes candidate labels into a hierarchical structure and uses one or more paths within the hierarchy as the ground-truth labels, which has been applied to various downstream tasks, e.g., sentiment analysis and harmful text detection. Existing works often involve data-driven models that are trained on large-scale datasets. However, creating annotated datasets is labor-intensive and time-consuming. To address this issue, recent work has focused on the few-shot HTC task, where each class has only a few samples, e.g., 5. These approaches perform classification at each layer separately and leverage the prompt learning capability of pre-trained models like BERT. However, we find that these methods always neglect the inter-layer relationships. To solve this problem, we propose a new model called Bidirectional Path Constraint by Label Weighting ( Bpc-lw ). Its basic idea is to use a pre-defined label embedding matrix and a feed-forward neural network for information propagation between layers, while also designing a bidirectional label weighting method to constrain the predictions of each layer to be along the same path in the label hierarchy. In addition, we employ a contrastive learning-based method to enhance the discriminative capacity of the hierarchical embeddings. We compare our proposed method with recent few-shot HTC baseline models across 3 benchmark datasets, and the experimental results demonstrate the effectiveness of Bpc-lw . • We argue that current methods neglect inter-layer information propagation. • We design label embedding matrices and feed-forward neural networks to transmit the label features. • Extensive experiments are conducted.

Cross-domain text classification aims to transfer models from label-rich source domains to label-poor target domains, giving it a wide range of practical applications. Many approaches promote cross-domain generalization by capturing domaininvariant features. However, these methods rely on unlabeled samples provided by the target domains, which renders the model ineffective when the target domain is agnostic. Furthermore, the models are easily disturbed by shortcut learning in the source domain, which also hinders the improvement of domain generalization ability. To solve the aforementioned issues, this paper proposes TACIT, a target domain agnostic feature disentanglement framework which adaptively decouples robust and unrobust features by Variational Auto-Encoders. Additionally, to encourage the separation of unrobust features from robust features, we design a feature distillation task that compels unrobust features to approximate the output of the teacher. The teacher model is trained with a few easy samples that are easy to carry potential unknown shortcuts. Experimental results verify that our framework achieves comparable results to state-of-the-art baselines while utilizing only source domain data.

Deep Neural Networks (DNNs) are rather restrictive in long-tailed data, since they commonly exhibit an under-representation for minority classes. Various remedies have been proposed to tackle this problem from different perspectives, but they ignore the impact of the density of Backbone Features (BFs) on this issue. Through representation learning, DNNs can map BFs into dense clusters in feature space, while the features of minority classes often show sparse clusters. In practical applications, these features are discretely mapped or even cross the decision boundary resulting in misclassification. Inspired by this observation, we propose a simple and generic method, namely Feature Clusters Compression (FCC), to increase the density of BFs by compressing backbone feature clusters. The proposed FCC can be easily achieved by only multiplying original BFs by a scaling factor in training phase, which establishes a linear compression relationship between the original and multiplied features, and forces DNNs to map the former into denser clusters. In test phase, we directly feed original features without multiplying the factor to the classifier, such that BFs of test samples are mapped closer together and do not easily cross the decision boundary. Meanwhile, FCC can be friendly combined with existing long-tailed methods and further boost them. We apply FCC to numerous state-of-the-art methods and evaluate them on widely used long-tailed benchmark datasets. Extensive experiments fully verify the effectiveness and generality of our method. Code is available at https://github.com/lijian16/FCC.

Ontology serves as the foundation for the underlying representation of knowledge. In order to achieve the sharing of knowledge across languages, ontologies that are typically only represented in English must be translated into different languages. Building a domain-specific translation system is necessary for ontology due to the extremely specialized vocabulary and absence of contextual information. In this paper, we introduce cross-lingual agreement to alleviate the aforementioned issues. We propose a method for representing ontology labels that constructs agreement constraint objects by minimizing the distances between source and target sides. We also integrate with adversarial learning to reduce the difference between the ontology label and the hypothesis generated by the translation model during the fine-tuning. Finally, the agreement modeling strategy is incorporated into the decoding phase to guide the generation of translation candidates. Experiments on the four domains English-to-German ontology show that the proposed method achieves significant improvements over the baselines.

Interactive argument pair identification is an emerging research task for argument mining, aiming to identify whether two arguments are interactively related.It is pointed out that the context of the argument is essential to improve identification performance.However, current context-based methods achieve limited improvements since the entire context typically contains much irrelevant information.In this paper, we propose a simple contrastive learning framework to solve this problem by extracting valuable information from the context.This framework can construct hard argumentcontext samples and obtain a robust and uniform representation by introducing contrastive learning.We also propose an argument-context extraction module to enhance information extraction by discarding irrelevant blocks.The experimental results show that our method achieves the state-of-the-art performance on the benchmark dataset.Further analysis demonstrates the effectiveness of our proposed modules and visually displays more compact semantic representations.The code is available at GitHub 1 .

Federated Learning (FL) is an emerging privacy-aware machine learning technique that applies successfully to the collaborative learning of global models for Human Activity Recognition (HAR). As of now, the applications of FL for HAR assume that the data associated with diverse individuals follow the same distribution. However, this assumption is impractical in real-world scenarios where the same activity is frequently performed differently by different individuals. To tackle this issue, we propose FedMAT, a Federated Multi-task ATtention framework for HAR, which extracts and fuses shared as well as individual-specific multi-modal sensor data features. Specifically, we treat the HAR problem associated with each individual as a different task and train a federated multi-task model, composed of a shared feature representation network in a central server plus multiple individual-specific networks with attention modules stored in decentralized nodes. In this architecture, the attention module operates as a mask that allows to learn individual-specific features from the global model, whilst simultaneously allowing for features to be shared among different individuals. We conduct extensive experiments based on publicly available HAR datasets, which are collected in both controlled environments and real-world scenarios. Numeric results verify that our proposed FedMAT significantly outperforms baselines not only in generalizing to existing individuals but also in adapting to new individuals.