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Yehudit Aperstein

Publications and source records attributed to Yehudit Aperstein.

36 records · Page 2Linked to original sources
arXiv2026

LLM-guided headline rewriting for clickability enhancement without clickbait

Enhancing reader engagement while preserving informational fidelity is a central challenge in controllable text generation for news media. Optimizing news headlines for reader engagement is often conflated with clickbait, resulting in exaggerated or misleading phrasing that undermines editorial trust. We frame clickbait not as a separate stylistic category, but as an extreme outcome of disproportionate amplification of otherwise legitimate engagement cues. Based on this view, we formulate headline rewriting as a controllable generation problem, where specific engagement-oriented linguistic attributes are selectively strengthened under explicit constraints on semantic faithfulness and proportional emphasis. We present a guided headline rewriting framework built on a large language model (LLM) that uses the Future Discriminators for Generation (FUDGE) paradigm for inference-time control. The LLM is steered by two auxiliary guide models: (1) a clickbait scoring model that provides negative guidance to suppress excessive stylistic amplification, and (2) an engagement-attribute model that provides positive guidance aligned with target clickability objectives. Both guides are trained on neutral headlines drawn from a curated real-world news corpus. At the same time, clickbait variants are generated synthetically by rewriting these original headlines using an LLM under controlled activation of predefined engagement tactics. By adjusting guidance weights at inference time, the system generates headlines along a continuum from neutral paraphrases to more engaging yet editorially acceptable formulations. The proposed framework provides a principled approach for studying the trade-off between attractiveness, semantic preservation, and clickbait avoidance, and supports responsible LLM-based headline optimization in journalistic settings.

cs.CL↗
arXiv2025

Explainable Semantic Text Relations: A Question-Answering Framework for Comparing Document Content

Understanding semantic relations between two texts is crucial for many information and document management tasks, in which one must determine whether the content fully overlaps, is completely superseded by another document, or overlaps only partially, with unique information in each. Beyond establishing this relation, it is equally important to provide explainable outputs that specify which pieces of information are present, missing, or newly added between the text pair. In this study, we formally define semantic relations between two texts through the set-theoretic relation between their respective Answerable Question Sets (AQS), the sets of questions each text can answer. Under this formulation, Semantic Text Relation (STR), such as equivalence, inclusion, and mutual overlap, becomes a well-defined set relation between the corresponding texts' AQSs. The set differences between the AQSs also serve as an explanation or diagnostic tool for identifying how the information in the texts diverges. Using this definition, we construct a synthetic benchmark that captures fine-grained informational relations through controlled paraphrasing and deliberate information removal supported by AQS manipulations. We then use this dataset to evaluate several discriminative and generative models for classifying text pairs into STR categories, assessing how well different model architectures capture semantic relations beyond surface-level similarity. We publicly release both the dataset and the data generation code to support further research.

cs.CL↗
arXiv2025

Improving Deep Tabular Learning

Tabular data remain a dominant form of real-world information but pose persistent challenges for deep learning due to heterogeneous feature types, lack of natural structure, and limited label-preserving augmentations. As a result, ensemble models based on decision trees continue to dominate benchmark leaderboards. In this work, we introduce RuleNet, a transformer-based architecture specifically designed for deep tabular learning. RuleNet incorporates learnable rule embeddings in a decoder, a piecewise linear quantile projection for numerical features, and feature masking ensembles for robustness and uncertainty estimation. Evaluated on eight benchmark datasets, RuleNet matches or surpasses state-of-the-art tree-based methods in most cases, while remaining computationally efficient, offering a practical neural alternative for tabular prediction tasks.

cs.LG↗
arXiv2025

When Curiosity Signals Danger: Predicting Health Crises Through Online Medication Inquiries

Online medical forums are a rich and underutilized source of insight into patient concerns, especially regarding medication use. Some of the many questions users pose may signal confusion, misuse, or even the early warning signs of a developing health crisis. Detecting these critical questions that may precede severe adverse events or life-threatening complications is vital for timely intervention and improving patient safety. This study introduces a novel annotated dataset of medication-related questions extracted from online forums. Each entry is manually labelled for criticality based on clinical risk factors. We benchmark the performance of six traditional machine learning classifiers using TF-IDF textual representations, alongside three state-of-the-art large language model (LLM)-based classification approaches that leverage deep contextual understanding. Our results highlight the potential of classical and modern methods to support real-time triage and alert systems in digital health spaces. The curated dataset is made publicly available to encourage further research at the intersection of patient-generated data, natural language processing, and early warning systems for critical health events. The dataset and benchmark are available at: https://github.com/Dvora-coder/LLM-Medication-QA-Risk-Classifier-MediGuard.

cs.CL↗
arXiv2025

From Fuzzy Speech to Medical Insight: Benchmarking LLMs on Noisy Patient Narratives

The widespread adoption of large language models (LLMs) in healthcare raises critical questions about their ability to interpret patient-generated narratives, which are often informal, ambiguous, and noisy. Existing benchmarks typically rely on clean, structured clinical text, offering limited insight into model performance under realistic conditions. In this work, we present a novel synthetic dataset designed to simulate patient self-descriptions characterized by varying levels of linguistic noise, fuzzy language, and layperson terminology. Our dataset comprises clinically consistent scenarios annotated with ground-truth diagnoses, spanning a spectrum of communication clarity to reflect diverse real-world reporting styles. Using this benchmark, we fine-tune and evaluate several state-of-the-art models (LLMs), including BERT-based and encoder-decoder T5 models. To support reproducibility and future research, we release the Noisy Diagnostic Benchmark (NDB), a structured dataset of noisy, synthetic patient descriptions designed to stress-test and compare the diagnostic capabilities of large language models (LLMs) under realistic linguistic conditions. We made the benchmark available for the community: https://github.com/lielsheri/PatientSignal

cs.CL↗
arXiv2025

An Interpretable Benchmark for Clickbait Detection and Tactic Attribution

The proliferation of clickbait headlines poses significant challenges to the credibility of information and user trust in digital media. While recent advances in machine learning have improved the detection of manipulative content, the lack of explainability limits their practical adoption. This paper presents a model for explainable clickbait detection that not only identifies clickbait titles but also attributes them to specific linguistic manipulation strategies. We introduce a synthetic dataset generated by systematically augmenting real news headlines using a predefined catalogue of clickbait strategies. This dataset enables controlled experimentation and detailed analysis of model behaviour. We present a two-stage framework for automatic clickbait analysis comprising detection and tactic attribution. In the first stage, we compare a fine-tuned BERT classifier with large language models (LLMs), specifically GPT-4.0 and Gemini 2.4 Flash, under both zero-shot prompting and few-shot prompting enriched with illustrative clickbait headlines and their associated persuasive tactics. In the second stage, a dedicated BERT-based classifier predicts the specific clickbait strategies present in each headline. This work advances the development of transparent and trustworthy AI systems for combating manipulative media content. We share the dataset with the research community at https://github.com/LLM-HITCS25S/ClickbaitTacticsDetection

cs.CL↗
arXiv2025

Multi-pathology Chest X-ray Classification with Rejection Mechanisms

Overconfidence in deep learning models poses a significant risk in high-stakes medical imaging tasks, particularly in multi-label classification of chest X-rays, where multiple co-occurring pathologies must be detected simultaneously. This study introduces an uncertainty-aware framework for chest X-ray diagnosis based on a DenseNet-121 backbone, enhanced with two selective prediction mechanisms: entropy-based rejection and confidence interval-based rejection. Both methods enable the model to abstain from uncertain predictions, improving reliability by deferring ambiguous cases to clinical experts. A quantile-based calibration procedure is employed to tune rejection thresholds using either global or class-specific strategies. Experiments conducted on three large public datasets (PadChest, NIH ChestX-ray14, and MIMIC-CXR) demonstrate that selective rejection improves the trade-off between diagnostic accuracy and coverage, with entropy-based rejection yielding the highest average AUC across all pathologies. These results support the integration of selective prediction into AI-assisted diagnostic workflows, providing a practical step toward safer, uncertainty-aware deployment of deep learning in clinical settings.

eess.IV↗
arXiv2025

Reading Between the Lines: Classifying Resume Seniority with Large Language Models

Accurately assessing candidate seniority from resumes is a critical yet challenging task, complicated by the prevalence of overstated experience and ambiguous self-presentation. In this study, we investigate the effectiveness of large language models (LLMs), including fine-tuned BERT architectures, for automating seniority classification in resumes. To rigorously evaluate model performance, we introduce a hybrid dataset comprising both real-world resumes and synthetically generated hard examples designed to simulate exaggerated qualifications and understated seniority. Using the dataset, we evaluate the performance of Large Language Models in detecting subtle linguistic cues associated with seniority inflation and implicit expertise. Our findings highlight promising directions for enhancing AI-driven candidate evaluation systems and mitigating bias introduced by self-promotional language. The dataset is available for the research community at https://bit.ly/4mcTovt

cs.CL↗
arXiv2025

Enhancing Classification of Streaming Data with Image Distillation

This study tackles the challenge of efficiently classifying streaming data in envi-ronments with limited memory and computational resources. It delves into the application of data distillation as an innovative approach to improve the precision of streaming image data classification. By focusing on distilling essential features from data streams, our method aims to minimize computational demands while preserving crucial information for accurate classification. Our investigation com-pares this approach against traditional algorithms like Hoeffding Trees and Adap-tive Random Forest, adapted through embeddings for image data. The Distillation Based Classification (DBC) demonstrated superior performance, achieving a 73.1% accuracy rate, surpassing both traditional methods and Reservoir Sam-pling Based Classification (RBC) technique. This marks a significant advance-ment in streaming data classification, showcasing the effectiveness of our method in processing complex data streams and setting a new standard for accuracy and efficiency.

cs.CV↗
arXiv2025

From Joy to Fear: A Benchmark of Emotion Estimation in Pop Song Lyrics

The emotional content of song lyrics plays a pivotal role in shaping listener experiences and influencing musical preferences. This paper investigates the task of multi-label emotional attribution of song lyrics by predicting six emotional intensity scores corresponding to six fundamental emotions. A manually labeled dataset is constructed using a mean opinion score (MOS) approach, which aggregates annotations from multiple human raters to ensure reliable ground-truth labels. Leveraging this dataset, we conduct a comprehensive evaluation of several publicly available large language models (LLMs) under zero-shot scenarios. Additionally, we fine-tune a BERT-based model specifically for predicting multi-label emotion scores. Experimental results reveal the relative strengths and limitations of zero-shot and fine-tuned models in capturing the nuanced emotional content of lyrics. Our findings highlight the potential of LLMs for emotion recognition in creative texts, providing insights into model selection strategies for emotion-based music information retrieval applications. The labeled dataset is available at https://github.com/LLM-HITCS25S/LyricsEmotionAttribution.

cs.CL↗
arXiv2025

Code Review Without Borders: Evaluating Synthetic vs. Real Data for Review Recommendation

Automating the decision of whether a code change requires manual review is vital for maintaining software quality in modern development workflows. However, the emergence of new programming languages and frameworks creates a critical bottleneck: while large volumes of unlabelled code are readily available, there is an insufficient amount of labelled data to train supervised models for review classification. We address this challenge by leveraging Large Language Models (LLMs) to translate code changes from well-resourced languages into equivalent changes in underrepresented or emerging languages, generating synthetic training data where labelled examples are scarce. We assume that although LLMs have learned the syntax and semantics of new languages from available unlabelled code, they have yet to fully grasp which code changes are considered significant or review-worthy within these emerging ecosystems. To overcome this, we use LLMs to generate synthetic change examples and train supervised classifiers on them. We systematically compare the performance of these classifiers against models trained on real labelled data. Our experiments across multiple GitHub repositories and language pairs demonstrate that LLM-generated synthetic data can effectively bootstrap review recommendation systems, narrowing the performance gap even in low-resource settings. This approach provides a scalable pathway to extend automated code review capabilities to rapidly evolving technology stacks, even in the absence of annotated data.

cs.SE↗
arXiv2025

Do Large Language Models Need Intent? Revisiting Response Generation Strategies for Service Assistant

In the era of conversational AI, generating accurate and contextually appropriate service responses remains a critical challenge. A central question remains: Is explicit intent recognition a prerequisite for generating high-quality service responses, or can models bypass this step and produce effective replies directly? This paper conducts a rigorous comparative study to address this fundamental design dilemma. Leveraging two publicly available service interaction datasets, we benchmark several state-of-the-art language models, including a fine-tuned T5 variant, across both paradigms: Intent-First Response Generation and Direct Response Generation. Evaluation metrics encompass both linguistic quality and task success rates, revealing surprising insights into the necessity or redundancy of explicit intent modelling. Our findings challenge conventional assumptions in conversational AI pipelines, offering actionable guidelines for designing more efficient and effective response generation systems.

cs.CL↗
arXiv2025

Stitching the Story: Creating Panoramic Incident Summaries from Body-Worn Footage

First responders widely adopt body-worn cameras to document incident scenes and support post-event analysis. However, reviewing lengthy video footage is impractical in time-critical situations. Effective situational awareness demands a concise visual summary that can be quickly interpreted. This work presents a computer vision pipeline that transforms body-camera footage into informative panoramic images summarizing the incident scene. Our method leverages monocular Simultaneous Localization and Mapping (SLAM) to estimate camera trajectories and reconstruct the spatial layout of the environment. Key viewpoints are identified by clustering camera poses along the trajectory, and representative frames from each cluster are selected. These frames are fused into spatially coherent panoramic images using multi-frame stitching techniques. The resulting summaries enable rapid understanding of complex environments and facilitate efficient decision-making and incident review.

cs.CV↗
arXiv2025

Beyond Words: Interjection Classification for Improved Human-Computer Interaction

In the realm of human-computer interaction, fostering a natural dialogue between humans and machines is paramount. A key, often overlooked, component of this dialogue is the use of interjections such as "mmm" and "hmm". Despite their frequent use to express agreement, hesitation, or requests for information, these interjections are typically dismissed as "non-words" by Automatic Speech Recognition (ASR) engines. Addressing this gap, we introduce a novel task dedicated to interjection classification, a pioneer in the field to our knowledge. This task is challenging due to the short duration of interjection signals and significant inter- and intra-speaker variability. In this work, we present and publish a dataset of interjection signals collected specifically for interjection classification. We employ this dataset to train and evaluate a baseline deep learning model. To enhance performance, we augment the training dataset using techniques such as tempo and pitch transformation, which significantly improve classification accuracy, making models more robust. The interjection dataset, a Python library for the augmentation pipeline, baseline model, and evaluation scripts, are available to the research community.

cs.HC↗
arXiv2025

PTEENet: Post-Trained Early-Exit Neural Networks Augmentation for Inference Cost Optimization

For many practical applications, a high computational cost of inference over deep network architectures might be unacceptable. A small degradation in the overall inference accuracy might be a reasonable price to pay for a significant reduction in the required computational resources. In this work, we describe a method for introducing "shortcuts" into the DNN feedforward inference process by skipping costly feedforward computations whenever possible. The proposed method is based on the previously described BranchyNet (Teerapittayanon et al., 2016) and the EEnet (Demir, 2019) architectures that jointly train the main network and early exit branches. We extend those methods by attaching branches to pre-trained models and, thus, eliminating the need to alter the original weights of the network. We also suggest a new branch architecture based on convolutional building blocks to allow enough training capacity when applied on large DNNs. The proposed architecture includes confidence heads that are used for predicting the confidence level in the corresponding early exits. By defining adjusted thresholds on these confidence extensions, we can control in real-time the amount of data exiting from each branch and the overall tradeoff between speed and accuracy of our model. In our experiments, we evaluate our method using image datasets (SVHN and CIFAR10) and several DNN architectures (ResNet, DenseNet, VGG) with varied depth. Our results demonstrate that the proposed method enables us to reduce the average inference computational cost and further controlling the tradeoff between the model accuracy and the computation cost.

cs.LG↗
arXiv2024

XAI-Based Detection of Adversarial Attacks on Deepfake Detectors

We introduce a novel methodology for identifying adversarial attacks on deepfake detectors using eXplainable Artificial Intelligence (XAI). In an era characterized by digital advancement, deepfakes have emerged as a potent tool, creating a demand for efficient detection systems. However, these systems are frequently targeted by adversarial attacks that inhibit their performance. We address this gap, developing a defensible deepfake detector by leveraging the power of XAI. The proposed methodology uses XAI to generate interpretability maps for a given method, providing explicit visualizations of decision-making factors within the AI models. We subsequently employ a pretrained feature extractor that processes both the input image and its corresponding XAI image. The feature embeddings extracted from this process are then used for training a simple yet effective classifier. Our approach contributes not only to the detection of deepfakes but also enhances the understanding of possible adversarial attacks, pinpointing potential vulnerabilities. Furthermore, this approach does not change the performance of the deepfake detector. The paper demonstrates promising results suggesting a potential pathway for future deepfake detection mechanisms. We believe this study will serve as a valuable contribution to the community, sparking much-needed discourse on safeguarding deepfake detectors.

cs.CR↗
arXiv2023

Offline Skill Graph (OSG): A Framework for Learning and Planning using Offline Reinforcement Learning Skills

Reinforcement Learning has received wide interest due to its success in competitive games. Yet, its adoption in everyday applications is limited (e.g. industrial, home, healthcare, etc.). In this paper, we address this limitation by presenting a framework for planning over offline skills and solving complex tasks in real-world environments. Our framework is comprised of three modules that together enable the agent to learn from previously collected data and generalize over it to solve long-horizon tasks. We demonstrate our approach by testing it on a robotic arm that is required to solve complex tasks.

cs.RO↗