pwc-archive/papers-with-abstracts huggingface dataset:
Generative AI Takes a Statistics Exam: A Comparison of Performance between ChatGPT3.5, ChatGPT4, and ChatGPT4o-mini
Authors: Monnie McGee, Bivin Sadler
Date: 2025-01-15 00:00:00

Many believe that use of generative AI as a private tutor has the potential to shrink access and achievement gaps between students and schools with abundant resources versus those with fewer resources. Shrinking the gap is possible only if paid and free versions of the platforms perform with the same accuracy. In this experiment, we investigate the performance of GPT versions 3.5, 4.0, and 4o-mini on the same 16-question statistics exam given to a class of first-year graduate students. While we do not advocate using any generative AI platform to complete an exam, the use of exam questions allows us to explore aspects of ChatGPT's responses to typical questions that students might encounter in a statistics course. Results on accuracy indicate that GPT 3.5 would fail the exam, GPT4 would perform well, and GPT4o-mini would perform somewhere in between. While we acknowledge the existence of other Generative AI/LLMs, our discussion concerns only ChatGPT because it is the most widely used platform on college campuses at this time. We further investigate differences among the AI platforms in the answers for each problem using methods developed for text analytics, such as reading level evaluation and topic modeling. Results indicate that GPT3.5 and 4o-mini have characteristics that are more similar than either of them have with GPT4.
Towards Federated Multi-Armed Bandit Learning for Content Dissemination using Swarm of UAVs
Authors: Amit Kumar Bhuyan, Hrishikesh Dutta, Subir Biswas
Date: 2025-01-15 00:00:00

This paper introduces an Unmanned Aerial Vehicle - enabled content management architecture that is suitable for critical content access in communities of users that are communication-isolated during diverse types of disaster scenarios. The proposed architecture leverages a hybrid network of stationary anchor UAVs and mobile Micro-UAVs for ubiquitous content dissemination. The anchor UAVs are equipped with both vertical and lateral communication links, and they serve local users, while the mobile micro-ferrying UAVs extend coverage across communities with increased mobility. The focus is on developing a content dissemination system that dynamically learns optimal caching policies to maximize content availability. The core innovation is an adaptive content dissemination framework based on distributed Federated Multi-Armed Bandit learning. The goal is to optimize UAV content caching decisions based on geo-temporal content popularity and user demand variations. A Selective Caching Algorithm is also introduced to reduce redundant content replication by incorporating inter-UAV information sharing. This method strategically preserves the uniqueness in user preferences while amalgamating the intelligence across a distributed learning system. This approach improves the learning algorithm's ability to adapt to diverse user preferences. Functional verification and performance evaluation confirm the proposed architecture's utility across different network sizes, UAV swarms, and content popularity patterns.
Gradient Descent Converges Linearly to Flatter Minima than Gradient Flow in Shallow Linear Networks
Authors: Pierfrancesco Beneventano, Blake Woodworth
Date: 2025-01-15 00:00:00

We study the gradient descent (GD) dynamics of a depth-2 linear neural network with a single input and output. We show that GD converges at an explicit linear rate to a global minimum of the training loss, even with a large stepsize -- about $2/\textrm{sharpness}$. It still converges for even larger stepsizes, but may do so very slowly. We also characterize the solution to which GD converges, which has lower norm and sharpness than the gradient flow solution. Our analysis reveals a trade off between the speed of convergence and the magnitude of implicit regularization. This sheds light on the benefits of training at the ``Edge of Stability'', which induces additional regularization by delaying convergence and may have implications for training more complex models.
Augmenting Human-Annotated Training Data with Large Language Model Generation and Distillation in Open-Response Assessment
Authors: Conrad Borchers, Danielle R. Thomas, Jionghao Lin, Ralph Abboud, Kenneth R. Koedinger
Date: 2025-01-15 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, text-classification, Text Classification

Large Language Models (LLMs) like GPT-4o can help automate text classification tasks at low cost and scale. However, there are major concerns about the validity and reliability of LLM outputs. By contrast, human coding is generally more reliable but expensive to procure at scale. In this study, we propose a hybrid solution to leverage the strengths of both. We combine human-coded data and synthetic LLM-produced data to fine-tune a classical machine learning classifier, distilling both into a smaller BERT model. We evaluate our method on a human-coded test set as a validity measure for LLM output quality. In three experiments, we systematically vary LLM-generated samples' size, variety, and consistency, informed by best practices in LLM tuning. Our findings indicate that augmenting datasets with synthetic samples improves classifier performance, with optimal results achieved at an 80% synthetic to 20% human-coded data ratio. Lower temperature settings of 0.3, corresponding to less variability in LLM generations, produced more stable improvements but also limited model learning from augmented samples. In contrast, higher temperature settings (0.7 and above) introduced greater variability in performance estimates and, at times, lower performance. Hence, LLMs may produce more uniform output that classifiers overfit to earlier or produce more diverse output that runs the risk of deteriorating model performance through information irrelevant to the prediction task. Filtering out inconsistent synthetic samples did not enhance performance. We conclude that integrating human and LLM-generated data to improve text classification models in assessment offers a scalable solution that leverages both the accuracy of human coding and the variety of LLM outputs.
Multi-Class Traffic Assignment using Multi-View Heterogeneous Graph Attention Networks
Authors: Tong Liu, Hadi Meidani
Date: 2025-01-15 00:00:00
Tasks: Graph Attention

Solving traffic assignment problem for large networks is computationally challenging when conventional optimization-based methods are used. In our research, we develop an innovative surrogate model for a traffic assignment when multi-class vehicles are involved. We do so by employing heterogeneous graph neural networks which use a multiple-view graph attention mechanism tailored to different vehicle classes, along with additional links connecting origin-destination pairs. We also integrate the node-based flow conservation law into the loss function. As a result, our model adheres to flow conservation while delivering highly accurate predictions for link flows and utilization ratios. Through numerical experiments conducted on urban transportation networks, we demonstrate that our model surpasses traditional neural network approaches in convergence speed and predictive accuracy in both user equilibrium and system optimal versions of traffic assignment.
Rethinking Post-Training Quantization: Introducing a Statistical Pre-Calibration Approach
Authors: Alireza Ghaffari, Sharareh Younesian, Boxing Chen, Vahid Partovi Nia, Masoud Asgharian
Date: 2025-01-15 00:00:00
Tasks: Quantization

As Large Language Models (LLMs) become increasingly computationally complex, developing efficient deployment strategies, such as quantization, becomes crucial. State-of-the-art Post-training Quantization (PTQ) techniques often rely on calibration processes to maintain the accuracy of these models. However, while these calibration techniques can enhance performance in certain domains, they may not be as effective in others. This paper aims to draw attention to robust statistical approaches that can mitigate such issues. We propose a weight-adaptive PTQ method that can be considered a precursor to calibration-based PTQ methods, guiding the quantization process to preserve the distribution of weights by minimizing the Kullback-Leibler divergence between the quantized weights and the originally trained weights. This minimization ensures that the quantized model retains the Shannon information content of the original model to a great extent, guaranteeing robust and efficient deployment across many tasks. As such, our proposed approach can perform on par with most common calibration-based PTQ methods, establishing a new pre-calibration step for further adjusting the quantized weights with calibration. We show that our pre-calibration results achieve the same accuracy as some existing calibration-based PTQ methods on various LLMs.
Similarity-Quantized Relative Difference Learning for Improved Molecular Activity Prediction
Authors: Karina Zadorozhny, Kangway V. Chuang, Bharath Sathappan, Ewan Wallace, Vishnu Sresht, Colin A. Grambow
Date: 2025-01-15 00:00:00
Tasks: Activity Prediction, Benchmarking, Drug Discovery, Prediction

Accurate prediction of molecular activities is crucial for efficient drug discovery, yet remains challenging due to limited and noisy datasets. We introduce Similarity-Quantized Relative Learning (SQRL), a learning framework that reformulates molecular activity prediction as relative difference learning between structurally similar pairs of compounds. SQRL uses precomputed molecular similarities to enhance training of graph neural networks and other architectures, and significantly improves accuracy and generalization in low-data regimes common in drug discovery. We demonstrate its broad applicability and real-world potential through benchmarking on public datasets as well as proprietary industry data. Our findings demonstrate that leveraging similarity-aware relative differences provides an effective paradigm for molecular activity prediction.
Generative diffusion model with inverse renormalization group flows
Authors: Kanta Masuki, Yuto Ashida
Date: 2025-01-15 00:00:00
Tasks: Audio Synthesis, Denoising, Image Generation, model, Point Cloud Generation, Protein Structure Prediction

Diffusion models represent a class of generative models that produce data by denoising a sample corrupted by white noise. Despite the success of diffusion models in computer vision, audio synthesis, and point cloud generation, so far they overlook inherent multiscale structures in data and have a slow generation process due to many iteration steps. In physics, the renormalization group offers a fundamental framework for linking different scales and giving an accurate coarse-grained model. Here we introduce a renormalization group-based diffusion model that leverages multiscale nature of data distributions for realizing a high-quality data generation. In the spirit of renormalization group procedures, we define a flow equation that progressively erases data information from fine-scale details to coarse-grained structures. Through reversing the renormalization group flows, our model is able to generate high-quality samples in a coarse-to-fine manner. We validate the versatility of the model through applications to protein structure prediction and image generation. Our model consistently outperforms conventional diffusion models across standard evaluation metrics, enhancing sample quality and/or accelerating sampling speed by an order of magnitude. The proposed method alleviates the need for data-dependent tuning of hyperparameters in the generative diffusion models, showing promise for systematically increasing sample efficiency based on the concept of the renormalization group.
Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks
Authors: Shuang Cui, Yi Li, Jiangmeng Li, Xiongxin Tang, Bing Su, Fanjiang Xu, Hui Xiong
Date: 2025-01-15 00:00:00
Tasks: Deblurring, Image Defocus Deblurring, Test-time Adaptation

Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distribution inferences. In this work, we gauge the intrinsic reason behind the performance degradation, which is identified as the heterogeneity of lens-specific point spread functions. Empirical evidence supports this finding, motivating us to employ a continual test-time adaptation (CTTA) paradigm for SIDD. However, traditional CTTA methods, which primarily rely on entropy minimization, cannot sufficiently explore task-dependent information for pixel-level regression tasks like SIDD. To address this issue, we propose a novel Siamese networks-based continual test-time adaptation framework, which adapts source models to continuously changing target domains only requiring unlabeled target data in an online manner. To further mitigate semantically erroneous textures introduced by source SIDD models under severe degradation, we revisit the learning paradigm through a structural causal model and propose Causal Siamese networks (CauSiam). Our method leverages large-scale pre-trained vision-language models to derive discriminative universal semantic priors and integrates these priors into Siamese networks, ensuring causal identifiability between blurry inputs and restored images. Extensive experiments demonstrate that CauSiam effectively improves the generalization performance of existing SIDD methods in continuously changing domains.
OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking
Authors: Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang, Ningyu Zhang, Jiang Yong, Pengjun Xie, Fei Huang, Huajun Chen
Date: 2025-01-16 00:00:00
Tasks: Articles, Retrieval-augmented Generation

Machine writing with large language models often relies on retrieval-augmented generation. However, these approaches remain confined within the boundaries of the model's predefined scope, limiting the generation of content with rich information. Specifically, vanilla-retrieved information tends to lack depth, novelty, and suffers from redundancy, which negatively impacts the quality of generated articles, leading to shallow, unoriginal, and repetitive outputs. To address these issues, we propose OmniThink, a slow-thinking machine writing framework that emulates the human-like process of iterative expansion and reflection. The core idea behind OmniThink is to simulate the cognitive behavior of learners as they slowly deepen their knowledge of the topics. Experimental results demonstrate that OmniThink improves the knowledge density of generated articles without compromising metrics such as coherence and depth. Human evaluations and expert feedback further highlight the potential of OmniThink to address real-world challenges in the generation of long-form articles.
Enhancing Lexicon-Based Text Embeddings with Large Language Models
Authors: Yibin Lei, Tao Shen, Yu Cao, Andrew Yates
Date: 2025-01-16 00:00:00

Recent large language models (LLMs) have demonstrated exceptional performance on general-purpose text embedding tasks. While dense embeddings have dominated related research, we introduce the first Lexicon-based EmbeddiNgS (LENS) leveraging LLMs that achieve competitive performance on these tasks. Regarding the inherent tokenization redundancy issue and unidirectional attention limitations in traditional causal LLMs, LENS consolidates the vocabulary space through token embedding clustering, and investigates bidirectional attention and various pooling strategies. Specifically, LENS simplifies lexicon matching by assigning each dimension to a specific token cluster, where semantically similar tokens are grouped together, and unlocking the full potential of LLMs through bidirectional attention. Extensive experiments demonstrate that LENS outperforms dense embeddings on the Massive Text Embedding Benchmark (MTEB), delivering compact feature representations that match the sizes of dense counterparts. Notably, combining LENSE with dense embeddings achieves state-of-the-art performance on the retrieval subset of MTEB (i.e. BEIR).
Comparative Insights from 12 Machine Learning Models in Extracting Economic Ideology from Political Text
Authors: Jihed Ncib
Date: 2025-01-16 00:00:00
Tasks: General Knowledge

This study conducts a systematic assessment of the capabilities of 12 machine learning models and model variations in detecting economic ideology. As an evaluation benchmark, I use manifesto data spanning six elections in the United Kingdom and pre-annotated by expert and crowd coders. The analysis assesses the performance of several generative, fine-tuned, and zero-shot models at the granular and aggregate levels. The results show that generative models such as GPT-4o and Gemini 1.5 Flash consistently outperform other models against all benchmarks. However, they pose issues of accessibility and resource availability. Fine-tuning yielded competitive performance and offers a reliable alternative through domain-specific optimization. But its dependency on training data severely limits scalability. Zero-shot models consistently face difficulties with identifying signals of economic ideology, often resulting in negative associations with human coding. Using general knowledge for the domain-specific task of ideology scaling proved to be unreliable. Other key findings include considerable within-party variation, fine-tuning benefiting from larger training data, and zero-shot's sensitivity to prompt content. The assessments include the strengths and limitations of each model and derive best-practices for automated analyses of political content.
Domain Adaptation of Foundation LLMs for e-Commerce
Authors: Christian Herold, Michael Kozielski, Tala Bazazo, Pavel Petrushkov, Seyyed Hadi Hashemi, Patrycja Cieplicka, Dominika Basaj, Shahram Khadivi
Date: 2025-01-16 00:00:00
Tasks: Domain Adaptation

We present the e-Llama models: 8 billion and 70 billion parameter large language models that are adapted towards the e-commerce domain. These models are meant as foundation models with deep knowledge about e-commerce, that form a base for instruction- and fine-tuning. The e-Llama models are obtained by continuously pretraining the Llama 3.1 base models on 1 trillion tokens of domain-specific data. We discuss our approach and motivate our choice of hyperparameters with a series of ablation studies. To quantify how well the models have been adapted to the e-commerce domain, we define and implement a set of multilingual, e-commerce specific evaluation tasks. We show that, when carefully choosing the training setup, the Llama 3.1 models can be adapted towards the new domain without sacrificing significant performance on general domain tasks. We also explore the possibility of merging the adapted model and the base model for a better control of the performance trade-off between domains.
From Scarcity to Capability: Empowering Fake News Detection in Low-Resource Languages with LLMs
Authors: Hrithik Majumdar Shibu, Shrestha Datta, Md. Sumon Miah, Nasrullah Sami, Mahruba Sharmin Chowdhury, Md. Saiful Islam
Date: 2025-01-16 00:00:00
Tasks: Articles, Fact Checking, Fake News Detection

The rapid spread of fake news presents a significant global challenge, particularly in low-resource languages like Bangla, which lack adequate datasets and detection tools. Although manual fact-checking is accurate, it is expensive and slow to prevent the dissemination of fake news. Addressing this gap, we introduce BanFakeNews-2.0, a robust dataset to enhance Bangla fake news detection. This version includes 11,700 additional, meticulously curated fake news articles validated from credible sources, creating a proportional dataset of 47,000 authentic and 13,000 fake news items across 13 categories. In addition, we created a manually curated independent test set of 460 fake and 540 authentic news items for rigorous evaluation. We invest efforts in collecting fake news from credible sources and manually verified while preserving the linguistic richness. We develop a benchmark system utilizing transformer-based architectures, including fine-tuned Bidirectional Encoder Representations from Transformers variants (F1-87\%) and Large Language Models with Quantized Low-Rank Approximation (F1-89\%), that significantly outperforms traditional methods. BanFakeNews-2.0 offers a valuable resource to advance research and application in fake news detection for low-resourced languages. We publicly release our dataset and model on Github to foster research in this direction.
Stylomech: Unveiling Authorship via Computational Stylometry in English and Romanized Sinhala
Authors: Nabeelah Faumi, Adeepa Gunathilake, Benura Wickramanayake, Deelaka Dias, TGDK Sumanathilaka
Date: 2025-01-16 00:00:00
Tasks: Author Attribution, Authorship Attribution, Ethics

With the advent of Web 2.0, the development in social technology coupled with global communication systematically brought positive and negative impacts to society. Copyright claims and Author identification are deemed crucial as there has been a considerable amount of increase in content violation owing to the lack of proper ethics in society. The Author's attribution in both English and Romanized Sinhala became a major requirement in the last few decades. As an area largely unexplored, particularly within the context of Romanized Sinhala, the research contributes significantly to the field of computational linguistics. The proposed author attribution system offers a unique approach, allowing for the comparison of only two sets of text: suspect author and anonymous text, a departure from traditional methodologies which often rely on larger corpora. This work focuses on using the numerical representation of various pairs of the same and different authors allowing for, the model to train on these representations as opposed to text, this allows for it to apply to a multitude of authors and contexts, given that the suspected author text, and the anonymous text are of reasonable quality. By expanding the scope of authorship attribution to encompass diverse linguistic contexts, the work contributes to fostering trust and accountability in digital communication, especially in Sri Lanka. This research presents a pioneering approach to author attribution in both English and Romanized Sinhala, addressing a critical need for content verification and intellectual property rights enforcement in the digital age.
Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matrices
Authors: Hajime Kiyama, Taichi Aida, Mamoru Komachi, Toshinobu Ogiso, Hiroya Takamura, Daichi Mochihashi
Date: 2025-01-16 00:00:00
Tasks: Word Embeddings, Word Similarity

The meanings and relationships of words shift over time. This phenomenon is referred to as semantic shift. Research focused on understanding how semantic shifts occur over multiple time periods is essential for gaining a detailed understanding of semantic shifts. However, detecting change points only between adjacent time periods is insufficient for analyzing detailed semantic shifts, and using BERT-based methods to examine word sense proportions incurs a high computational cost. To address those issues, we propose a simple yet intuitive framework for how semantic shifts occur over multiple time periods by leveraging a similarity matrix between the embeddings of the same word through time. We compute a diachronic word similarity matrix using fast and lightweight word embeddings across arbitrary time periods, making it deeper to analyze continuous semantic shifts. Additionally, by clustering the similarity matrices for different words, we can categorize words that exhibit similar behavior of semantic shift in an unsupervised manner.