pwc-archive/papers-with-abstracts huggingface dataset:
TartuNLP at SemEval-2025 Task 5: Subject Tagging as Two-Stage Information Retrieval
Authors: Aleksei Dorkin, Kairit Sirts
Date: 2025-04-30 00:00:00
Tasks: Information Retrieval, Re-Ranking, Retrieval

We present our submission to the Task 5 of SemEval-2025 that aims to aid librarians in assigning subject tags to the library records by producing a list of likely relevant tags for a given document. We frame the task as an information retrieval problem, where the document content is used to retrieve subject tags from a large subject taxonomy. We leverage two types of encoder models to build a two-stage information retrieval system -- a bi-encoder for coarse-grained candidate extraction at the first stage, and a cross-encoder for fine-grained re-ranking at the second stage. This approach proved effective, demonstrating significant improvements in recall compared to single-stage methods and showing competitive results according to qualitative evaluation.
Improving Informally Romanized Language Identification
Authors: Adrian Benton, Alexander Gutkin, Christo Kirov, Brian Roark
Date: 2025-04-30 00:00:00
Tasks: Language Identification

The Latin script is often used to informally write languages with non-Latin native scripts. In many cases (e.g., most languages in India), there is no conventional spelling of words in the Latin script, hence there will be high spelling variability in written text. Such romanization renders languages that are normally easily distinguished based on script highly confusable, such as Hindi and Urdu. In this work, we increase language identification (LID) accuracy for romanized text by improving the methods used to synthesize training sets. We find that training on synthetic samples which incorporate natural spelling variation yields higher LID system accuracy than including available naturally occurring examples in the training set, or even training higher capacity models. We demonstrate new state-of-the-art LID performance on romanized text from 20 Indic languages in the Bhasha-Abhijnaanam evaluation set (Madhani et al., 2023a), improving test F1 from the reported 74.7% (using a pretrained neural model) to 85.4% using a linear classifier trained solely on synthetic data and 88.2% when also training on available harvested text.
Advancing Arabic Reverse Dictionary Systems: A Transformer-Based Approach with Dataset Construction Guidelines
Authors: Serry Sibaee, Samar Ahmed, Abdullah Al Harbi, Omer Nacar, Adel Ammar, Yasser Habashi, Wadii Boulila
Date: 2025-04-30 00:00:00
Tasks: Reverse Dictionary

This study addresses the critical gap in Arabic natural language processing by developing an effective Arabic Reverse Dictionary (RD) system that enables users to find words based on their descriptions or meanings. We present a novel transformer-based approach with a semi-encoder neural network architecture featuring geometrically decreasing layers that achieves state-of-the-art results for Arabic RD tasks. Our methodology incorporates a comprehensive dataset construction process and establishes formal quality standards for Arabic lexicographic definitions. Experiments with various pre-trained models demonstrate that Arabic-specific models significantly outperform general multilingual embeddings, with ARBERTv2 achieving the best ranking score (0.0644). Additionally, we provide a formal abstraction of the reverse dictionary task that enhances theoretical understanding and develop a modular, extensible Python library (RDTL) with configurable training pipelines. Our analysis of dataset quality reveals important insights for improving Arabic definition construction, leading to eight specific standards for building high-quality reverse dictionary resources. This work contributes significantly to Arabic computational linguistics and provides valuable tools for language learning, academic writing, and professional communication in Arabic.
Homa at SemEval-2025 Task 5: Aligning Librarian Records with OntoAligner for Subject Tagging
Authors: Hadi Bayrami Asl Tekanlou, Jafar Razmara, Mahsa Sanaei, Mostafa Rahgouy, Hamed Babaei Giglou
Date: 2025-04-30 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation, Semantic Similarity, Semantic Textual Similarity

This paper presents our system, Homa, for SemEval-2025 Task 5: Subject Tagging, which focuses on automatically assigning subject labels to technical records from TIBKAT using the Gemeinsame Normdatei (GND) taxonomy. We leverage OntoAligner, a modular ontology alignment toolkit, to address this task by integrating retrieval-augmented generation (RAG) techniques. Our approach formulates the subject tagging problem as an alignment task, where records are matched to GND categories based on semantic similarity. We evaluate OntoAligner's adaptability for subject indexing and analyze its effectiveness in handling multilingual records. Experimental results demonstrate the strengths and limitations of this method, highlighting the potential of alignment techniques for improving subject tagging in digital libraries.
RWKV-X: A Linear Complexity Hybrid Language Model
Authors: Haowen Hou, Zhiyi Huang, Kaifeng Tan, Rongchang Lu, Fei Richard Yu
Date: 2025-04-30 00:00:00
Tasks: Language Modeling, Language Modelling, model

In this paper, we introduce RWKV-X, a novel hybrid architecture that combines the efficiency of RWKV for short-range modeling with a sparse attention mechanism designed to capture long-range context. Unlike previous hybrid approaches that rely on full attention layers and retain quadratic complexity, RWKV-X achieves linear-time complexity in training and constant-time complexity in inference decoding. We demonstrate that RWKV-X, when continually pretrained on 64K-token sequences, achieves near-perfect accuracy on the 64K passkey retrieval benchmark. It consistently outperforms prior RWKV-7 models on long-context benchmarks, while maintaining strong performance on short-context tasks. These results highlight RWKV-X as a scalable and efficient backbone for general-purpose language modeling, capable of decoding sequences up to 1 million tokens with stable speed and memory usage. To facilitate further research and analysis, we have made the checkpoints and the associated code publicly accessible at: https://github.com/howard-hou/RWKV-X.
SeriesBench: A Benchmark for Narrative-Driven Drama Series Understanding
Authors: Chenkai Zhang, Yiming Lei, Zeming Liu, Haitao Leng, Shaoguo Liu, Tingting Gao, Qingjie Liu, Yunhong Wang
Date: 2025-04-30 00:00:00
Proceeding: CVPR 2025 1
Tasks: Video Understanding

With the rapid development of Multi-modal Large Language Models (MLLMs), an increasing number of benchmarks have been established to evaluate the video understanding capabilities of these models. However, these benchmarks focus on standalone videos and mainly assess "visual elements" like human actions and object states. In reality, contemporary videos often encompass complex and continuous narratives, typically presented as a series. To address this challenge, we propose SeriesBench, a benchmark consisting of 105 carefully curated narrative-driven series, covering 28 specialized tasks that require deep narrative understanding. Specifically, we first select a diverse set of drama series spanning various genres. Then, we introduce a novel long-span narrative annotation method, combined with a full-information transformation approach to convert manual annotations into diverse task formats. To further enhance model capacity for detailed analysis of plot structures and character relationships within series, we propose a novel narrative reasoning framework, PC-DCoT. Extensive results on SeriesBench indicate that existing MLLMs still face significant challenges in understanding narrative-driven series, while PC-DCoT enables these MLLMs to achieve performance improvements. Overall, our SeriesBench and PC-DCoT highlight the critical necessity of advancing model capabilities to understand narrative-driven series, guiding the future development of MLLMs. SeriesBench is publicly available at https://github.com/zackhxn/SeriesBench-CVPR2025.
The Distribution of Dependency Distance and Hierarchical Distance in Contemporary Written Japanese and Its Influencing Factors
Authors: Linxuan Wang, Shuiyuan Yu
Date: 2025-04-30 00:00:00
Tasks: Sentence

To explore the relationship between dependency distance (DD) and hierarchical distance (HD) in Japanese, we compared the probability distributions of DD and HD with and without sentence length fixed, and analyzed the changes in mean dependency distance (MDD) and mean hierarchical distance (MHD) as sentence length increases, along with their correlation coefficient based on the Balanced Corpus of Contemporary Written Japanese. It was found that the valency of the predicates is the underlying factor behind the trade-off relation between MDD and MHD in Japanese. Native speakers of Japanese regulate the linear complexity and hierarchical complexity through the valency of the predicates, and the relative sizes of MDD and MHD depend on whether the threshold of valency has been reached. Apart from the cognitive load, the valency of the predicates also affects the probability distributions of DD and HD. The effect of the valency of the predicates on the distribution of HD is greater than on that of DD, which leads to differences in their probability distributions and causes the mean of MDD to be lower than that of MHD.
OPAL: Visibility-aware LiDAR-to-OpenStreetMap Place Recognition via Adaptive Radial Fusion
Authors: Shuhao Kang, Martin Y. Liao, Yan Xia, Olaf Wysocki, Boris Jutzi, Daniel Cremers
Date: 2025-04-27 00:00:00
Tasks: Autonomous Navigation

LiDAR place recognition is a critical capability for autonomous navigation and cross-modal localization in large-scale outdoor environments. Existing approaches predominantly depend on pre-built 3D dense maps or aerial imagery, which impose significant storage overhead and lack real-time adaptability. In this paper, we propose OPAL, a novel network for LiDAR place recognition that leverages OpenStreetMap (OSM) as a lightweight and up-to-date prior. Our key innovation lies in bridging the domain disparity between sparse LiDAR scans and structured OSM data through two carefully designed components. First, a cross-modal visibility mask that identifies maximal observable regions from both modalities to guide feature learning. Second, an adaptive radial fusion module that dynamically consolidates radial features into discriminative global descriptors. Extensive experiments on the KITTI and KITTI-360 datasets demonstrate OPAL's superiority, achieving 15.98% higher recall at @1m threshold for top-1 retrieved matches, along with 12x faster inference speed compared to the state-of-the-art approach. Code and datasets will be publicly available.
Who Gets the Callback? Generative AI and Gender Bias
Authors: Sugat Chaturvedi, Rochana Chaturvedi
Date: 2025-04-30 00:00:00
Tasks: Fairness

Generative artificial intelligence (AI), particularly large language models (LLMs), is being rapidly deployed in recruitment and for candidate shortlisting. We audit several mid-sized open-source LLMs for gender bias using a dataset of 332,044 real-world online job postings. For each posting, we prompt the model to recommend whether an equally qualified male or female candidate should receive an interview callback. We find that most models tend to favor men, especially for higher-wage roles. Mapping job descriptions to the Standard Occupational Classification system, we find lower callback rates for women in male-dominated occupations and higher rates in female-associated ones, indicating occupational segregation. A comprehensive analysis of linguistic features in job ads reveals strong alignment of model recommendations with traditional gender stereotypes. To examine the role of recruiter identity, we steer model behavior by infusing Big Five personality traits and simulating the perspectives of historical figures. We find that less agreeable personas reduce stereotyping, consistent with an agreeableness bias in LLMs. Our findings highlight how AI-driven hiring may perpetuate biases in the labor market and have implications for fairness and diversity within firms.
Emotional Analysis of Fashion Trends Using Social Media and AI: Sentiment Analysis on Twitter for Fashion Trend Forecasting
Authors: Aayam Bansal, Agneya Tharun
Date: 2025-04-30 00:00:00
Tasks: Sentiment Analysis, Sentiment Classification

This study explores the intersection of fashion trends and social media sentiment through computational analysis of Twitter data using the T4SA (Twitter for Sentiment Analysis) dataset. By applying natural language processing and machine learning techniques, we examine how sentiment patterns in fashion-related social media conversations can serve as predictors for emerging fashion trends. Our analysis involves the identification and categorization of fashion-related content, sentiment classification with improved normalization techniques, time series decomposition, statistically validated causal relationship modeling, cross-platform sentiment comparison, and brand-specific sentiment analysis. Results indicate correlations between sentiment patterns and fashion theme popularity, with accessories and streetwear themes showing statistically significant rising trends. The Granger causality analysis establishes sustainability and streetwear as primary trend drivers, showing bidirectional relationships with several other themes. The findings demonstrate that social media sentiment analysis can serve as an effective early indicator of fashion trend trajectories when proper statistical validation is applied. Our improved predictive model achieved 78.35% balanced accuracy in sentiment classification, establishing a reliable foundation for trend prediction across positive, neutral, and negative sentiment categories.
SimPRIVE: a Simulation framework for Physical Robot Interaction with Virtual Environments
Authors: Federico Nesti, Gianluca D'Amico, Mauro Marinoni, Giorgio Buttazzo
Date: 2025-04-30 00:00:00

The use of machine learning in cyber-physical systems has attracted the interest of both industry and academia. However, no general solution has yet been found against the unpredictable behavior of neural networks and reinforcement learning agents. Nevertheless, the improvements of photo-realistic simulators have paved the way towards extensive testing of complex algorithms in different virtual scenarios, which would be expensive and dangerous to implement in the real world. This paper presents SimPRIVE, a simulation framework for physical robot interaction with virtual environments, which operates as a vehicle-in-the-loop platform, rendering a virtual world while operating the vehicle in the real world. Using SimPRIVE, any physical mobile robot running on ROS 2 can easily be configured to move its digital twin in a virtual world built with the Unreal Engine 5 graphic engine, which can be populated with objects, people, or other vehicles with programmable behavior. SimPRIVE has been designed to accommodate custom or pre-built virtual worlds while being light-weight to contain execution times and allow fast rendering. Its main advantage lies in the possibility of testing complex algorithms on the full software and hardware stack while minimizing the risks and costs of a test campaign. The framework has been validated by testing a reinforcement learning agent trained for obstacle avoidance on an AgileX Scout Mini rover that navigates a virtual office environment where everyday objects and people are placed as obstacles. The physical rover moves with no collision in an indoor limited space, thanks to a LiDAR-based heuristic.
Efficient Domain-adaptive Continual Pretraining for the Process Industry in the German Language
Authors: Anastasia Zhukova, Christian E. Matt, Terry Ruas, Bela Gipp
Date: 2025-04-28 00:00:00
Tasks: Continual Pretraining, GPU, In-Context Learning, Language Modeling, Language Modelling

Domain-adaptive continual pretraining (DAPT) is a state-of-the-art technique that further trains a language model (LM) on its pretraining task, e.g., language masking. Although popular, it requires a significant corpus of domain-related data, which is difficult to obtain for specific domains in languages other than English, such as the process industry in the German language. This paper introduces an efficient approach called ICL-augmented pretraining or ICL-APT that leverages in-context learning (ICL) and k-nearest neighbors (kNN) to augment target data with domain-related and in-domain texts, significantly reducing GPU time while maintaining strong model performance. Our results show that this approach performs better than traditional DAPT by 3.5 points of the average IR metrics (e.g., mAP, MRR, and nDCG) and requires almost 4 times less computing time, providing a cost-effective solution for industries with limited computational capacity. The findings highlight the broader applicability of this framework to other low-resource industries, making NLP-based solutions more accessible and feasible in production environments.
UAV-VLN: End-to-End Vision Language guided Navigation for UAVs
Authors: Pranav Saxena, Nishant Raghuvanshi, Neena Goveas
Date: 2025-04-30 00:00:00
Tasks: Common Sense Reasoning, Instruction Following, Navigate, Vision-Language Navigation

A core challenge in AI-guided autonomy is enabling agents to navigate realistically and effectively in previously unseen environments based on natural language commands. We propose UAV-VLN, a novel end-to-end Vision-Language Navigation (VLN) framework for Unmanned Aerial Vehicles (UAVs) that seamlessly integrates Large Language Models (LLMs) with visual perception to facilitate human-interactive navigation. Our system interprets free-form natural language instructions, grounds them into visual observations, and plans feasible aerial trajectories in diverse environments. UAV-VLN leverages the common-sense reasoning capabilities of LLMs to parse high-level semantic goals, while a vision model detects and localizes semantically relevant objects in the environment. By fusing these modalities, the UAV can reason about spatial relationships, disambiguate references in human instructions, and plan context-aware behaviors with minimal task-specific supervision. To ensure robust and interpretable decision-making, the framework includes a cross-modal grounding mechanism that aligns linguistic intent with visual context. We evaluate UAV-VLN across diverse indoor and outdoor navigation scenarios, demonstrating its ability to generalize to novel instructions and environments with minimal task-specific training. Our results show significant improvements in instruction-following accuracy and trajectory efficiency, highlighting the potential of LLM-driven vision-language interfaces for safe, intuitive, and generalizable UAV autonomy.
Retrieval-Enhanced Few-Shot Prompting for Speech Event Extraction
Authors: Máté Gedeon
Date: 2025-04-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Event Extraction, Retrieval, Semantic Similarity, Semantic Textual Similarity, speech-recognition, Speech Recognition

Speech Event Extraction (SpeechEE) is a challenging task that lies at the intersection of Automatic Speech Recognition (ASR) and Natural Language Processing (NLP), requiring the identification of structured event information from spoken language. In this work, we present a modular, pipeline-based SpeechEE framework that integrates high-performance ASR with semantic search-enhanced prompting of Large Language Models (LLMs). Our system first classifies speech segments likely to contain events using a hybrid filtering mechanism including rule-based, BERT-based, and LLM-based models. It then employs few-shot LLM prompting, dynamically enriched via semantic similarity retrieval, to identify event triggers and extract corresponding arguments. We evaluate the pipeline using multiple LLMs (Llama3-8B, GPT-4o-mini, and o1-mini) highlighting significant performance gains with o1-mini, which achieves 63.3% F1 on trigger classification and 27.8% F1 on argument classification, outperforming prior benchmarks. Our results demonstrate that pipeline approaches, when empowered by retrieval-augmented LLMs, can rival or exceed end-to-end systems while maintaining interpretability and modularity. This work provides practical insights into LLM-driven event extraction and opens pathways for future hybrid models combining textual and acoustic features.
Does the Prompt-based Large Language Model Recognize Students' Demographics and Introduce Bias in Essay Scoring?
Authors: Kaixun Yang, Mladen Raković, Dragan Gašević, Guanliang Chen
Date: 2025-04-30 00:00:00
Tasks: Automated Essay Scoring, Fairness, Language Modeling, Language Modelling, Large Language Model

Large Language Models (LLMs) are widely used in Automated Essay Scoring (AES) due to their ability to capture semantic meaning. Traditional fine-tuning approaches required technical expertise, limiting accessibility for educators with limited technical backgrounds. However, prompt-based tools like ChatGPT have made AES more accessible, enabling educators to obtain machine-generated scores using natural-language prompts (i.e., the prompt-based paradigm). Despite advancements, prior studies have shown bias in fine-tuned LLMs, particularly against disadvantaged groups. It remains unclear whether such biases persist or are amplified in the prompt-based paradigm with cutting-edge tools. Since such biases are believed to stem from the demographic information embedded in pre-trained models (i.e., the ability of LLMs' text embeddings to predict demographic attributes), this study explores the relationship between the model's predictive power of students' demographic attributes based on their written works and its predictive bias in the scoring task in the prompt-based paradigm. Using a publicly available dataset of over 25,000 students' argumentative essays, we designed prompts to elicit demographic inferences (i.e., gender, first-language background) from GPT-4o and assessed fairness in automated scoring. Then we conducted multivariate regression analysis to explore the impact of the model's ability to predict demographics on its scoring outcomes. Our findings revealed that (i) prompt-based LLMs can somewhat infer students' demographics, particularly their first-language backgrounds, from their essays; (ii) scoring biases are more pronounced when the LLM correctly predicts students' first-language background than when it does not; and (iii) scoring error for non-native English speakers increases when the LLM correctly identifies them as non-native.