Xiangrong Zhu, Yuexiang Xie, Yi Liu, Yaliang Li, Wei Hu. 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.

Multi-aspect controllable text generation aims to control the generated texts in attributes from multiple aspects (e.g., "positive" from sentiment and "sport" from topic).For ease of obtaining training samples, existing works neglect attribute correlations formed by the intertwining of different attributes.Particularly, the stereotype formed by imbalanced attribute correlations significantly affects multi-aspect control.In this paper, we propose MAGIC, a new multi-aspect controllable text generation method with disentangled counterfactual augmentation.We alleviate the issue of imbalanced attribute correlations during training using counterfactual feature vectors in the attribute latent space by disentanglement.During inference, we enhance attribute correlations by target-guided counterfactual augmentation to further improve multi-aspect control.Experiments show that MAGIC outperforms stateof-the-art baselines in both imbalanced and balanced attribute correlation scenarios.

Federated Learning (FL) is a novel distributed, privacy-preserving machine learning paradigm. Conventional FL suffers from drawbacks such as single point of failure and client drift. Blockchain is a distributed computing architecture famous for decentralization, transparency, and traceability. Incorporating blockchain as the underlying basis for FL decentralizes the FL process and brings opportunities to resolve the drawbacks. However, there still remain challenges to fulfilling FL with blockchain, regarding effectiveness, efficiency, and security. In this paper, we propose a new blockchain system for FL, called FedChain. To mitigate client drift and accelerate training, we present a clustered semi-asynchronous method for model aggregation. To optimize the local training in FL, we introduce a knowledge transfer method using other clients on the peer-to-peer network of blockchain. Moreover, we implement an access control mechanism to store and transmit models safely and efficiently. Extensive experiments on various benchmark datasets show that FedChain achieves superior results in accuracy, convergence, throughput, and latency.

Federated Learning (FL) recently emerges as a paradigm to train a global machine learning model across distributed clients without sharing raw data. Knowledge Graph (KG) embedding represents KGs in a continuous vector space, serving as the backbone of many knowledge-driven applications. As a promising combination, federated KG embedding can fully take advantage of knowledge learned from different clients while preserving the privacy of local data. However, realistic problems such as data heterogeneity and knowledge forgetting still remain to be concerned. In this paper, we propose FedLU, a novel FL framework for heterogeneous KG embedding learning and unlearning. To cope with the drift between local optimization and global convergence caused by data heterogeneity, we propose mutual knowledge distillation to transfer local knowledge to global, and absorb global knowledge back. Moreover, we present an unlearning method based on cognitive neuroscience, which combines retroactive interference and passive decay to erase specific knowledge from local clients and propagate to the global model by reusing knowledge distillation. We construct new datasets for assessing realistic performance of the state-of-the-arts. Extensive experiments show that FedLU achieves superior results in both link prediction and knowledge forgetting.

Code sharing and reuse is a widespread use practice in software engineering. Although a vast amount of open-source Python code is accessible on many online platforms, programmers often find it difficult to restore a successful runtime environment. Previous studies validated automatic inference of Python dependencies using pre-built knowledge bases. However, these studies do not cover sufficient knowledge to accurately match the Python code and also ignore the potential conflicts between their inferred dependencies, thus resulting in a low success rate of inference. In this paper, we propose PyCRE, a new approach to automatically inferring Python compatible runtime environments with domain knowledge graph (KG). Specifically, we design a domain-specific ontology for Python third-party packages and construct KGs for over 10,000 popular packages in Python 2 and Python 3. PyCRE discovers candidate libraries by measuring the matching degree between the known libraries and the third-party resources used in target code. For the NP-complete problem of dependency solving, we propose a heuristic graph traversal algorithm to efficiently guarantee the compatibility between packages. PyCRE achieves superior performance on a real-world dataset and efficiently resolves nearly half more import errors than previous methods.