OpenRaiser/Intern-Atlas huggingface dataset:
Unlocking the Potential of Multiple BERT Models for Bangla Question Answering in NCTB Textbooks
Venue: arXiv cs.CL 2024
Year: 2024

Evaluating text comprehension in educational settings is critical for understanding student performance and improving curricular effectiveness. This study investigates the capability of state-of-the-art language models-RoBERTa Base, Bangla-BERT, and BERT Base-in automatically assessing Bangla passage-based question-answering from the National Curriculum and Textbook Board (NCTB) textbooks for classes 6-10. A dataset of approximately 3,000 Bangla passage-based question-answering instances was compiled, and the models were evaluated using F1 Score and Exact Match (EM) metrics across various hyperparameter configurations. Our findings revealed that Bangla-BERT consistently outperformed the other models, achieving the highest F1 (0.75) and EM (0.53) scores, particularly with smaller batch sizes, the inclusion of stop words, and a moderate learning rate. In contrast, RoBERTa Base demonstrated the weakest performance, with the lowest F1 (0.19) and EM (0.27) scores under certain configurations. The results underscore the importance of fine-tuning hyperparameters for optimizing model performance and highlight the potential of machine learning models in evaluating text comprehension in educational contexts. However, limitations such as dataset size, spelling inconsistencies, and computational constraints emphasize the need for further research to enhance the robustness and applicability of these models. This study lays the groundwork for the future development of automated evaluation systems in educational institutions, providing critical insights into model performance in the context of Bangla text comprehension.
A Survey on the Principles of Persuasion as a Social Engineering Strategy in Phishing
Venue: arXiv cs.CR 2024
Year: 2024

Research shows that phishing emails often utilize persuasion techniques, such as social proof, liking, consistency, authority, scarcity, and reciprocity to gain trust to obtain sensitive information or maliciously infect devices. The link between principles of persuasion and social engineering attacks, particularly in phishing email attacks, is an important topic in cyber security as they are the common and effective method used by cybercriminals to obtain sensitive information or access computer systems. This survey paper concluded that spear phishing, a targeted form of phishing, has been found to be specifically effective as attackers can tailor their messages to the specific characteristics, interests, and vulnerabilities of their targets. Understanding the uses of the principles of persuasion in spear phishing is key to the effective defence against it and eventually its elimination. This survey paper systematically summarizes and presents the current state of the art in understanding the use of principles of persuasion in phishing. Through a systematic review of the existing literature, this survey paper identifies a significant gap in the understanding of the impact of principles of persuasion as a social engineering strategy in phishing attacks and highlights the need for further research in this area.
SHARQ: Explainability Framework for Association Rules on Relational Data
Venue: arXiv cs.DB 2024
Year: 2024

Association rules are an important technique for gaining insights over large relational datasets consisting of tuples of elements (i.e. attribute-value pairs). However, it is difficult to explain the relative importance of data elements with respect to the rules in which they appear. This paper develops a measure of an element's contribution to a set of association rules based on Shapley values, denoted SHARQ (ShApley Rules Quantification). As is the case with many Shapely-based computations, the cost of a naive calculation of the score is exponential in the number of elements. To that end, we present an efficient framework for computing the exact SharQ value of a single element whose running time is practically linear in the number of rules. Going one step further, we develop an efficient multi-element SHARQ algorithm which amortizes the cost of the single element SHARQ calculation over a set of elements. Based on the definition of SHARQ for elements we describe two additional use cases for association rules explainability: rule importance and attribute importance. Extensive experiments over a novel benchmark dataset containing 45 instances of mined rule sets show the effectiveness of our approach.
Distilling Fine-grained Sentiment Understanding from Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Fine-grained sentiment analysis (FSA) aims to extract and summarize user opinions from vast opinionated text. Recent studies demonstrate that large language models (LLMs) possess exceptional sentiment understanding capabilities. However, directly deploying LLMs for FSA applications incurs high inference costs. Therefore, this paper investigates the distillation of fine-grained sentiment understanding from LLMs into small language models (SLMs). We prompt LLMs to examine and interpret the sentiments of given reviews and then utilize the generated content to pretrain SLMs. Additionally, we develop a comprehensive FSA benchmark to evaluate both SLMs and LLMs. Extensive experiments on this benchmark reveal that: (1) distillation significantly enhances the performance of SLMs in FSA tasks, achieving a 6.00\% improvement in $F_1$-score, and the distilled model can outperform Llama-2-7b with only 220M parameters; (2) distillation equips SLMs with excellent zero-shot sentiment classification capabilities, enabling them to match or even exceed their teacher models. These results suggest that distillation from LLMs is a highly promising direction for FSA. We will release our code, data, and pretrained model weights at https://github.com/HITSZ-HLT/FSA-Distillation.
Exploring Text Representations for Online Misinformation
Venue: arXiv cs.CL 2024
Year: 2024

Mis- and disinformation, commonly collectively called fake news, continue to menace society. Perhaps, the impact of this age-old problem is presently most plain in politics and healthcare. However, fake news is affecting an increasing number of domains. It takes many different forms and continues to shapeshift as technology advances. Though it arguably most widely spreads in textual form, e.g., through social media posts and blog articles. Thus, it is imperative to thwart the spread of textual misinformation, which necessitates its initial detection. This thesis contributes to the creation of representations that are useful for detecting misinformation. Firstly, it develops a novel method for extracting textual features from news articles for misinformation detection. These features harness the disparity between the thematic coherence of authentic and false news stories. In other words, the composition of themes discussed in both groups significantly differs as the story progresses. Secondly, it demonstrates the effectiveness of topic features for fake news detection, using classification and clustering. Clustering is particularly useful because it alleviates the need for a labelled dataset, which can be labour-intensive and time-consuming to amass. More generally, it contributes towards a better understanding of misinformation and ways of detecting it using Machine Learning and Natural Language Processing.
Comparing analytic and data-driven approaches to parameter identifiability: A power systems case study
Venue: arXiv cs.LG 2024
Year: 2024

Parameter identifiability refers to the capability of accurately inferring the parameter values of a model from its observations (data). Traditional analysis methods exploit analytical properties of the closed form model, in particular sensitivity analysis, to quantify the response of the model predictions to variations in parameters. Techniques developed to analyze data, specifically manifold learning methods, have the potential to complement, and even extend the scope of the traditional analytical approaches. We report on a study comparing and contrasting analytical and data-driven approaches to quantify parameter identifiability and, importantly, perform parameter reduction tasks. We use the infinite bus synchronous generator model, a well-understood model from the power systems domain, as our benchmark problem. Our traditional analysis methods use the Fisher Information Matrix to quantify parameter identifiability analysis, and the Manifold Boundary Approximation Method to perform parameter reduction. We compare these results to those arrived at through data-driven manifold learning schemes: Output - Diffusion Maps and Geometric Harmonics. For our test case, we find that the two suites of tools (analytical when a model is explicitly available, as well as data-driven when the model is lacking and only measurement data are available) give (correct) comparable results; these results are also in agreement with traditional analysis based on singular perturbation theory. We then discuss the prospects of using data-driven methods for such model analysis.
AgreeMate: Teaching LLMs to Haggle
Venue: arXiv cs.CL 2024
Year: 2024

We introduce AgreeMate, a framework for training Large Language Models (LLMs) to perform strategic price negotiations through natural language. We apply recent advances to a negotiation setting where two agents (i.e. buyer or seller) use natural language to bargain on goods using coarse actions. Specifically, we present the performance of Large Language Models when used as agents within a decoupled (modular) bargaining architecture. We demonstrate that using prompt engineering, fine-tuning, and chain-of-thought prompting enhances model performance, as defined by novel metrics. We use attention probing to show model attention to semantic relationships between tokens during negotiations.
Predicting Time Series of Networked Dynamical Systems without Knowing Topology
Venue: arXiv cs.LG 2024
Year: 2024

Many real-world complex systems, such as epidemic spreading networks and ecosystems, can be modeled as networked dynamical systems that produce multivariate time series. Learning the intrinsic dynamics from observational data is pivotal for forecasting system behaviors and making informed decisions. However, existing methods for modeling networked time series often assume known topologies, whereas real-world networks are typically incomplete or inaccurate, with missing or spurious links that hinder precise predictions. Moreover, while networked time series often originate from diverse topologies, the ability of models to generalize across topologies has not been systematically evaluated. To address these gaps, we propose a novel framework for learning network dynamics directly from observed time-series data, when prior knowledge of graph topology or governing dynamical equations is absent. Our approach leverages continuous graph neural networks with an attention mechanism to construct a latent topology, enabling accurate reconstruction of future trajectories for network states. Extensive experiments on real and synthetic networks demonstrate that our model not only captures dynamics effectively without topology knowledge but also generalizes to unseen time series originating from diverse topologies.
Evaluating authorship disambiguation quality through anomaly analysis on researchers' career transition
Venue: arXiv cs.DL 2024
Year: 2024

Authorship disambiguation is crucial for advancing studies in science of science. However, assessing the quality of authorship disambiguation in large-scale databases remains challenging since it is difficult to manually curate a gold-standard dataset that contains disambiguated authors. Through estimating the timing of when 5.8 million biomedical researchers became independent Principal Investigators (PIs) with authorship metadata extracted from the OpenAlex -- the largest open-source bibliometric database -- we unexpectedly discovered an anomaly: over 60% of researchers appeared as the last authors in their first career year. We hypothesized that this improbable finding results from poor name disambiguation, suggesting that such an anomaly may serve as an indicator of low-quality authorship disambiguation. Our findings indicated that authors who lack affiliation information, which makes it more difficult to disambiguate, were far more likely to exhibit this anomaly compared to those who included their affiliation information. In contrast, authors with Open Researcher and Contributor ID (ORCID) -- expected to have higher quality disambiguation -- showed significantly lower anomaly rates. We further applied this approach to examine the authorship disambiguation quality by gender over time, and we found that the quality of disambiguation for female authors was lower than that for male authors before 2010, suggesting that gender disparity findings based on pre-2010 data may require careful reexamination. Our results provide a framework for systematically evaluating authorship disambiguation quality in various contexts, facilitating future improvements in efforts to authorship disambiguation.
Attack-in-the-Chain: Bootstrapping Large Language Models for Attacks Against Black-box Neural Ranking Models
Venue: arXiv cs.IR 2024
Year: 2024

Neural ranking models (NRMs) have been shown to be highly effective in terms of retrieval performance. Unfortunately, they have also displayed a higher degree of sensitivity to attacks than previous generation models. To help expose and address this lack of robustness, we introduce a novel ranking attack framework named Attack-in-the-Chain, which tracks interactions between large language models (LLMs) and NRMs based on chain-of-thought (CoT) prompting to generate adversarial examples under black-box settings. Our approach starts by identifying anchor documents with higher ranking positions than the target document as nodes in the reasoning chain. We then dynamically assign the number of perturbation words to each node and prompt LLMs to execute attacks. Finally, we verify the attack performance of all nodes at each reasoning step and proceed to generate the next reasoning step. Empirical results on two web search benchmarks show the effectiveness of our method.
Torque-Aware Momentum
Venue: arXiv cs.LG 2024
Year: 2024

Efficiently exploring complex loss landscapes is key to the performance of deep neural networks. While momentum-based optimizers are widely used in state-of-the-art setups, classical momentum can still struggle with large, misaligned gradients, leading to oscillations. To address this, we propose Torque-Aware Momentum (TAM), which introduces a damping factor based on the angle between the new gradients and previous momentum, stabilizing the update direction during training. Empirical results show that TAM, which can be combined with both SGD and Adam, enhances exploration, handles distribution shifts more effectively, and improves generalization performance across various tasks, including image classification and large language model fine-tuning, when compared to classical momentum-based optimizers.
FOR: Finetuning for Object Level Open Vocabulary Image Retrieval
Venue: arXiv cs.CV 2024
Year: 2024

As working with large datasets becomes standard, the task of accurately retrieving images containing objects of interest by an open set textual query gains practical importance. The current leading approach utilizes a pre-trained CLIP model without any adaptation to the target domain, balancing accuracy and efficiency through additional post-processing. In this work, we propose FOR: Finetuning for Object-centric Open-vocabulary Image Retrieval, which allows finetuning on a target dataset using closed-set labels while keeping the visual-language association crucial for open vocabulary retrieval. FOR is based on two design elements: a specialized decoder variant of the CLIP head customized for the intended task, and its coupling within a multi-objective training framework. Together, these design choices result in a significant increase in accuracy, showcasing improvements of up to 8 mAP@50 points over SoTA across three datasets. Additionally, we demonstrate that FOR is also effective in a semi-supervised setting, achieving impressive results even when only a small portion of the dataset is labeled.
GSAVS: Gaussian Splatting-based Autonomous Vehicle Simulator
Venue: arXiv cs.RO 2024
Year: 2024

Modern autonomous vehicle simulators feature an ever-growing library of assets, including vehicles, buildings, roads, pedestrians, and more. While this level of customization proves beneficial when creating virtual urban environments, this process becomes cumbersome when intending to train within a digital twin or a duplicate of a real scene. Gaussian splatting emerged as a powerful technique in scene reconstruction and novel view synthesis, boasting high fidelity and rendering speeds. In this paper, we introduce GSAVS, an autonomous vehicle simulator that supports the creation and development of autonomous vehicle models. Every asset within the simulator is a 3D Gaussian splat, including the vehicles and the environment. However, the simulator runs within a classical 3D engine, rendering 3D Gaussian splats in real-time. This allows the simulator to utilize the photorealism that 3D Gaussian splatting boasts while providing the customization and ease of use of a classical 3D engine.
Improving the Readability of Automatically Generated Tests using Large Language Models
Venue: arXiv cs.SE 2024
Year: 2024

Search-based test generators are effective at producing unit tests with high coverage. However, such automatically generated tests have no meaningful test and variable names, making them hard to understand and interpret by developers. On the other hand, large language models (LLMs) can generate highly readable test cases, but they are not able to match the effectiveness of search-based generators, in terms of achieved code coverage. In this paper, we propose to combine the effectiveness of search-based generators with the readability of LLM generated tests. Our approach focuses on improving test and variable names produced by search-based tools, while keeping their semantics (i.e., their coverage) unchanged. Our evaluation on nine industrial and open source LLMs show that our readability improvement transformations are overall semantically-preserving and stable across multiple repetitions. Moreover, a human study with ten professional developers, show that our LLM-improved tests are as readable as developer-written tests, regardless of the LLM employed.
Computing Approximate Graph Edit Distance via Optimal Transport
Venue: arXiv cs.LG 2024
Year: 2024

Given a graph pair $(G^1, G^2)$, graph edit distance (GED) is defined as the minimum number of edit operations converting $G^1$ to $G^2$. GED is a fundamental operation widely used in many applications, but its exact computation is NP-hard, so the approximation of GED has gained a lot of attention. Data-driven learning-based methods have been found to provide superior results compared to classical approximate algorithms, but they directly fit the coupling relationship between a pair of vertices from their vertex features. We argue that while pairwise vertex features can capture the coupling cost (discrepancy) of a pair of vertices, the vertex coupling matrix should be derived from the vertex-pair cost matrix through a more well-established method that is aware of the global context of the graph pair, such as optimal transport. In this paper, we propose an ensemble approach that integrates a supervised learning-based method and an unsupervised method, both based on optimal transport. Our learning method, GEDIOT, is based on inverse optimal transport that leverages a learnable Sinkhorn algorithm to generate the coupling matrix. Our unsupervised method, GEDGW, models GED computation as a linear combination of optimal transport and its variant, Gromov-Wasserstein discrepancy, for node and edge operations, respectively, which can be solved efficiently without needing the ground truth. Our ensemble method, GEDHOT, combines GEDIOT and GEDGW to further boost the performance. Extensive experiments demonstrate that our methods significantly outperform the existing methods in terms of the performance of GED computation, edit path generation, and model generalizability.
Overview of MWE history, challenges, and horizons: standing at the 20th anniversary of the MWE workshop series via MWE-UD2024
Venue: arXiv cs.CL 2024
Year: 2024

Starting in 2003 when the first MWE workshop was held with ACL in Sapporo, Japan, this year, the joint workshop of MWE-UD co-located with the LREC-COLING 2024 conference marked the 20th anniversary of MWE workshop events over the past nearly two decades. Standing at this milestone, we look back to this workshop series and summarise the research topics and methodologies researchers have carried out over the years. We also discuss the current challenges that we are facing and the broader impacts/synergies of MWE research within the CL and NLP fields. Finally, we give future research perspectives. We hope this position paper can help researchers, students, and industrial practitioners interested in MWE get a brief but easy understanding of its history, current, and possible future.
Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty
Venue: arXiv cs.LG 2024
Year: 2024

Uncertainty-aware deep learning (DL) models recently gained attention in fault diagnosis as a way to promote the reliable detection of faults when out-of-distribution (OOD) data arise from unseen faults (epistemic uncertainty) or the presence of noise (aleatoric uncertainty). In this paper, we present the first comprehensive comparative study of state-of-the-art uncertainty-aware DL architectures for fault diagnosis in rotating machinery, where different scenarios affected by epistemic uncertainty and different types of aleatoric uncertainty are investigated. The selected architectures include sampling by dropout, Bayesian neural networks, and deep ensembles. Moreover, to distinguish between in-distribution and OOD data in the different scenarios two uncertainty thresholds, one of which is introduced in this paper, are alternatively applied. Our empirical findings offer guidance to practitioners and researchers who have to deploy real-world uncertainty-aware fault diagnosis systems. In particular, they reveal that, in the presence of epistemic uncertainty, all DL models are capable of effectively detecting, on average, a substantial portion of OOD data across all the scenarios. However, deep ensemble models show superior performance, independently of the uncertainty threshold used for discrimination. In the presence of aleatoric uncertainty, the noise level plays an important role. Specifically, low noise levels hinder the models' ability to effectively detect OOD data. Even in this case, however, deep ensemble models exhibit a milder degradation in performance, dominating the others. These achievements, combined with their shorter inference time, make deep ensemble architectures the preferred choice.