Large Language Models (LLMs) often produce fluent but factually incorrect responses, a phenomenon known as hallucination. Abstention, where the model chooses not to answer and instead outputs phrases such as "I don't know", is a common safeguard. However, existing abstention methods typically rely on post-generation signals, such as generation variations or feedback, which limits their ability to prevent unreliable responses in advance. In this paper, we introduce Aspect-Based Causal Abstention (ABCA), a new framework that enables early abstention by analysing the internal diversity of LLM knowledge through causal inference. This diversity reflects the multifaceted nature of parametric knowledge acquired from various sources, representing diverse aspects such as disciplines, legal contexts, or temporal frames. ABCA estimates causal effects conditioned on these aspects to assess the reliability of knowledge relevant to a given query. Based on these estimates, we enable two types of abstention: Type-1, where aspect effects are inconsistent (knowledge conflict), and Type-2, where aspect effects consistently support abstention (knowledge insufficiency). Experiments on standard benchmarks demonstrate that ABCA improves abstention reliability, achieves state-of-the-art performance, and enhances the interpretability of abstention decisions.

Large language models (LLMs) are increasingly used for recommendation reranking, but their listwise predictions can depend on the order in which candidates are presented. This creates a mismatch between the set-based nature of recommendation and the sequence-based computation of decoder-only LLMs, where permuting an otherwise identical candidate set can change item scores and final rankings. Such order sensitivity makes LLM-based rerankers difficult to rely on, since rankings may reflect prompt serialization rather than user preference. We propose InvariRank, a permutation-invariant listwise reranking framework that addresses this dependence at the architectural level. InvariRank blocks cross-candidate attention with a structured attention mask and negates position-induced scoring changes through shared positional framing under Rotary Positional Embeddings (RoPE). Combined with a listwise learning-to-rank objective, the model scores all candidates in a single forward pass, avoiding permutation-based invariance training objectives that require multiple permutations of a candidate set. Experiments on recommendation benchmarks show that InvariRank maintains competitive ranking effectiveness while producing stable rankings across candidate permutations. The results suggest that architectural invariance is a practical route to reliable and efficient LLM-based recommendation reranking. The source code is at https://github.com/ejbito/InvariRank.

Abstract Detecting Autism Spectrum Disorder (ASD) using Eye Tracking (ET) datasets is a challenging task and has been a long-standing problem. Recently, there has been a trend of developing ASD diagnosis models based on machine learning (ML), especially deep learning techniques. In this paper, we show that these existing methods still struggle to make accurate diagnoses in few-shot learning (FSL) settings, where the data available for training is limited in amount and imbalanced in nature. To address this challenge, we propose a model, named ConASD, for effective diagnosis of ASD under the FSL setting. The proposed model is a two-stage framework: it first trains an encoder for ET images using supervised contrastive learning, followed by fine-tuning a classifier for final diagnosis. With the contrastive learning strategy, the pre-trained encoder can better capture the discriminative features of the eye-tracking images, even with limited training data, and ultimately leads to better diagnosis accuracy and better generalization to unseen data. We evaluate the proposed ConASD model using two real-world ET datasets. The results demonstrate that ConASD outperforms existing approaches, particularly in few-shot scenarios, by up-to 7% improvement in terms of F1 scores. The results in this paper highlight the potential of using contrastive learning as a powerful tool, particularly in real-world medical scenarios where class imbalance is frequent and the data is limited.

Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness through the lens of spectral graph theory, aiming to reconcile fairness and utility within the framework of spectral graph learning. We explore the correlation between sensitive features and spectrum in GNNs, using theoretical analysis to delineate the similarity between original sensitive features and those after convolution under different spectra. Our analysis reveals a reduction in the impact of similarity when the eigenvectors associated with the largest magnitude eigenvalue exhibit directional similarity. Based on these theoretical insights, we propose FUGNN, a novel spectral graph learning approach that harmonizes the conflict between fairness and utility. FUGNN ensures algorithmic fairness and utility by truncating the spectrum and optimizing eigenvector distribution during the encoding process. The fairness-aware eigenvector selection reduces the impact of convolution on sensitive features while concurrently minimizing the sacrifice of utility. FUGNN further optimizes the distribution of eigenvectors through a transformer architecture. By incorporating the optimized spectrum into the graph convolution network, FUGNN effectively learns node representations. Experiments on six real-world datasets demonstrate the superiority of FUGNN over baseline methods. The codes are available at https://github.com/yushuowiki/FUGNN.

Understanding a meme is a challenging task, due to the metaphorical information contained in the meme that requires intricate interpretation to grasp its intended meaning fully. In previous works, attempts have been made to facilitate computational understanding of memes through introducing human-annotated metaphors as extra input features into machine learning models. However, these approaches mainly focus on formulating linguistic representation of a metaphor (extracted from the texts appearing in memes), while ignoring the connection between the metaphor and corresponding visual features (e.g., objects in meme images). In this paper, we argue that a more comprehensive understanding of memes can only be achieved through a joint modelling of both visual and linguistic features of memes. To this end, we propose an approach to generate Multimodal Metaphorical feature for Meme Classification, named MMMC. MMMC derives visual characteristics from linguistic attributes of metaphorical concepts, which more effectively convey the underlying metaphorical concept, leveraging a text-conditioned generative adversarial network. The linguistic and visual features are then integrated into a set of multimodal metaphorical features for classification purpose. We perform extensive experiments on a benchmark metaphorical meme dataset, MET-Meme. Experimental results show that MMMC significantly outperforms existing baselines on the task of emotion classification and intention detection. Our code and dataset are available at https://github.com/liaolianfoka/MMMC.

Aparna Elangovan, Jiayuan He, Yuan Li, Karin Verspoor. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Contrastive learning, which learns data representations by contrasting similar and dissimilar instances, has achieved great success in various domains including natural language processing (NLP). Recently, it has been demonstrated that incorporating class labels into contrastive learning, i.e., supervised contrastive learning (SCL), can further enhance the quality of the learned data representations. Although several works have shown empirically that incorporating SCL into classification models leads to better performance, the mechanism of how SCL works for classification is less studied. In this paper, we first investigate how SCL facilitates the classifier learning, where we show that the contrastive region, i.e., the data instances involved in each contrasting operation, has a crucial link to the mechanism of SCL. We reveal that the vanilla SCL is suboptimal since its behavior can be altered by variances in class distributions. Based on this finding, we propose aFocusedContrastiveLoss (FoCL) for classification. Compared with SCL, FoCL defines a finer contrastive region, focusing on the data instances surrounding decision boundaries. We conduct extensive experiments on three NLP tasks: text classification, named entity recognition, and relation extraction. Experimental results show consistent and significant improvements of FoCL over strong baselines on various benchmark datasets, especially in few-shot scenarios.

The emergence of 6G wireless networks brings a lot of potential in advanced wireless applications, but also faces plenty of challenges. This work aims to address the challenges posed by the emerging 6G networks, specifically in the terahertz band (252-325 GHz). The goal is to meet the demanding requirements of high data rates, low latency, and increased reliability. To overcome this, this work proposes an ML model for enhanced signal processing that incorporates domain knowledge of the terahertz field into the traditional ML architecture. The proposed model is demonstrated to be able to increase linearity of the received signal by 68.3% compared to the Volterra filtering method, and a linearity increase of 23.9% compared to a benchmark ResNet model without domain knowledge.

Public datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP).However, the presence of overlap between the train and test datasets can lead to inflated results, inadvertently evaluating the model's ability to memorize and interpreting it as the ability to generalize.In addition, such data sets may not provide an effective indicator of the performance of these methods in real world scenarios.We identify leakage of training data into test data on several publicly available datasets used to evaluate NLP tasks, including named entity recognition and relation extraction, and study them to assess the impact of that leakage on the model's ability to memorize versus generalize.The human SHBG proximal promoter was analyzed by DNase I footprinting, and the functional significance of 6 footprinted regions (FP1-FP6) within the proximal promoter was studied in human HepG2 hepatoblastoma cells.100.00

Biaoyan Fang, Christian Druckenbrodt, Saber A Akhondi, Jiayuan He, Timothy Baldwin, Karin Verspoor. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.

Next Point-of-Interest (POI) recommendation aims to rank a list of POIs by their attractiveness to users based on the users' historical records of POI visits. This task is challenging, because user preferences may be influenced by various contextual factors. In this paper, we consider the temporal contextual factor, i.e., the time of users' POI visits. Previous attempts for modelling the impact of temporal contexts can be categorized into two groups: factorization based methods and recurrent neural network based methods. The first group adds a time dimension to their latent recommendation spaces, which may suffer from the data sparsity problem due to the additional dimension. The second group uses time-aware contextual gates to update the hidden and cell states in RNNs, which may have limited capability in capturing long-range temporal dynamics. In this paper, we propose a time -modulated s elf-attentive network (TimeSAN) for next POI recommendation. This model learns the relevance between a user's next POI visit and her historical visits via the self-attention mechanism, where the relevance is modulated by the temporal contextual influence. The learned time-aware relevance is further fused with users' long-term interests to provide final recommendations. We conduct extensive experiments on real-world datasets. The results confirm that TimeSAN outperforms previous methods consistently and significantly in recommendation accuracy, while attaining a high model training efficiency.

Trip recommendation is an important location-based service that helps relieve users from the time and efforts for trip planning. It aims to recommend a sequence of places of interest (POIs) for a user to visit that maximizes the user's satisfaction. When adding a POI to a recommended trip, it is essential to understand the context of the recommendation, including the POI popularity, other POIs co-occurring in the trip, and the preferences of the user. These contextual factors are learned separately in existing studies, while in reality, they jointly impact on a user's choice of POI visits. In this study, we propose a POI embedding model to jointly learn the impact of these contextual factors. We call the learned POI embedding a context-aware POI embedding. To showcase the effectiveness of this embedding, we apply it to generate trip recommendations given a user and a time budget. We propose two trip recommendation algorithms based on our context-aware POI embedding. The first algorithm finds the exact optimal trip by transforming and solving the trip recommendation problem as an integer linear programming problem. To achieve a high computation efficiency, the second algorithm finds a heuristically optimal trip based on adaptive large neighborhood search. We perform extensive experiments on real datasets. The results show that our proposed algorithms consistently outperform state-of-the-art algorithms in trip recommendation quality, with an advantage of up to 43% in F_1-score.

The Gaussian process (GP) model is a powerful tool for regression problems. However, the high computational costs of the GP model has constrained its applications over large-scale data sets. To overcome this limitation, aggregation models employ distributed GP submodels (experts) for parallel training and predicting, and then merge the predictions of all submodels to produce an approximated result. The state-of-the-art aggregation models are based on Bayesian committee machines, where a prior is assumed at the start and then updated by each submodel. In this paper, we investigate the impact of the prior on the accuracy of aggregations. We propose a query-aware Bayesian committee machine (QBCM). The QBCM model partitions the testing data (i.e., queries) into subsets, and incorporates a query-aware prior when merging the predictions of submodels. This model improves the prediction accuracy, while retaining the advantages of aggregation models, i.e., closed-form inference and parallelizability. We conduct both theoretical analysis and empirical experiments on real data. The results confirm the effectiveness and efficiency of the proposed model QBCM.

The k nearest neighbor (kNN) query in road networks is a traditional query type in spatial databases. This query has found new applications in the fast-growing location-based services, e.g., finding the k nearest Uber cars of a user for ridesharing. KNN queries in these applications are non-trivial to process due to the frequent location updates of data objects (e.g., movements of the cars). This calls for novel spatial indexes with high efficiency in not only query processing but also update handling. To address this need, we propose an index structure that uses a "lazy update" strategy to reduce the costs of update handling without sacrificing query efficiency or answer accuracy. We cache the location updates of data objects and only update the corresponding entries in the index when they are queried. We further propose a kNN query algorithm based on this index. This algorithm takes advantage of the strengths of both the CPU and the GPU. It first identifies the queried region and updates the index over this region using the GPU. Then, it uses the GPU to query the index and produce a candidate result set, which is later refined by the CPU to obtain the final query answer. We conduct experiments on real data and compare the proposed algorithm with state-of-the-art kNN algorithms. The experimental results show that the proposed algorithm outperforms the baseline algorithms by orders of magnitude in query time.