pwc-archive/papers-with-abstracts huggingface dataset:
Exploring the Feasibility of Multilingual Grammatical Error Correction with a Single LLM up to 9B parameters: A Comparative Study of 17 Models
Authors: Dawid Wisniewski, Antoni Solarski, Artur Nowakowski
Date: 2025-05-09 00:00:00
Tasks: Grammatical Error Correction

Recent language models can successfully solve various language-related tasks, and many understand inputs stated in different languages. In this paper, we explore the performance of 17 popular models used to correct grammatical issues in texts stated in English, German, Italian, and Swedish when using a single model to correct texts in all those languages. We analyze the outputs generated by these models, focusing on decreasing the number of grammatical errors while keeping the changes small. The conclusions drawn help us understand what problems occur among those models and which models can be recommended for multilingual grammatical error correction tasks. We list six models that improve grammatical correctness in all four languages and show that Gemma 9B is currently the best performing one for the languages considered.
An Exploratory Analysis on the Explanatory Potential of Embedding-Based Measures of Semantic Transparency for Malay Word Recognition
Authors: M. Maziyah Mohamed, R. H. Baayen
Date: 2025-05-09 00:00:00
Tasks: Word Embeddings

Studies of morphological processing have shown that semantic transparency is crucial for word recognition. Its computational operationalization is still under discussion. Our primary objectives are to explore embedding-based measures of semantic transparency, and assess their impact on reading. First, we explored the geometry of complex words in semantic space. To do so, we conducted a t-distributed Stochastic Neighbor Embedding clustering analysis on 4,226 Malay prefixed words. Several clusters were observed for complex words varied by their prefix class. Then, we derived five simple measures, and investigated whether they were significant predictors of lexical decision latencies. Two sets of Linear Discriminant Analyses were run in which the prefix of a word is predicted from either word embeddings or shift vectors (i.e., a vector subtraction of the base word from the derived word). The accuracy with which the model predicts the prefix of a word indicates the degree of transparency of the prefix. Three further measures were obtained by comparing embeddings between each word and all other words containing the same prefix (i.e., centroid), between each word and the shift from their base word, and between each word and the predicted word of the Functional Representations of Affixes in Compositional Semantic Space model. In a series of Generalized Additive Mixed Models, all measures predicted decision latencies after accounting for word frequency, word length, and morphological family size. The model that included the correlation between each word and their centroid as a predictor provided the best fit to the data.
Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Models
Authors: Lennart Stöpler, Rufat Asadli, Mitja Nikolaus, Ryan Cotterell, Alex Warstadt
Date: 2025-05-09 00:00:00
Tasks: Language Acquisition, Question Answering

We propose a method for training language models in an interactive setting inspired by child language acquisition. In our setting, a speaker attempts to communicate some information to a listener in a single-turn dialogue and receives a reward if communicative success is achieved. Unlike earlier related work using image--caption data for interactive reference games, we operationalize communicative success in a more abstract language-only question--answering setting. First, we present a feasibility study demonstrating that our reward provides an indirect signal about grammaticality. Second, we conduct experiments using reinforcement learning to fine-tune language models. We observe that cognitively plausible constraints on the communication channel lead to interpretable changes in speaker behavior. However, we do not yet see improvements on linguistic evaluations from our training regime. We outline potential modifications to the task design and training configuration that could better position future work to use our methodology to observe the benefits of interaction on language learning in computational cognitive models.
NeoQA: Evidence-based Question Answering with Generated News Events
Authors: Max Glockner, Xiang Jiang, Leonardo F. R. Ribeiro, Iryna Gurevych, Markus Dreyer
Date: 2025-05-09 00:00:00
Tasks: Articles, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Evaluating Retrieval-Augmented Generation (RAG) in large language models (LLMs) is challenging because benchmarks can quickly become stale. Questions initially requiring retrieval may become answerable from pretraining knowledge as newer models incorporate more recent information during pretraining, making it difficult to distinguish evidence-based reasoning from recall. We introduce NeoQA (News Events for Out-of-training Question Answering), a benchmark designed to address this issue. To construct NeoQA, we generated timelines and knowledge bases of fictional news events and entities along with news articles and Q\&A pairs to prevent LLMs from leveraging pretraining knowledge, ensuring that no prior evidence exists in their training data. We propose our dataset as a new platform for evaluating evidence-based question answering, as it requires LLMs to generate responses exclusively from retrieved evidence and only when sufficient evidence is available. NeoQA enables controlled evaluation across various evidence scenarios, including cases with missing or misleading details. Our findings indicate that LLMs struggle to distinguish subtle mismatches between questions and evidence, and suffer from short-cut reasoning when key information required to answer a question is missing from the evidence, underscoring key limitations in evidence-based reasoning.
Summarisation of German Judgments in conjunction with a Class-based Evaluation
Authors: Bianca Steffes, Nils Torben Wiedemann, Alexander Gratz, Pamela Hochreither, Jana Elina Meyer, Katharina Luise Schilke
Date: 2025-05-09 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Large Language Model

The automated summarisation of long legal documents can be a great aid for legal experts in their daily work. We automatically create summaries (guiding principles) of German judgments by fine-tuning a decoder-based large language model. We enrich the judgments with information about legal entities before the training. For the evaluation of the created summaries, we define a set of evaluation classes which allows us to measure their language, pertinence, completeness and correctness. Our results show that employing legal entities helps the generative model to find the relevant content, but the quality of the created summaries is not yet sufficient for a use in practice.
Symbol-based entity marker highlighting for enhanced text mining in materials science with generative AI
Authors: Junhyeong Lee, Jong Min Yuk, Chan-Woo Lee
Date: 2025-05-09 00:00:00
Tasks: NER, scientific discovery

The construction of experimental datasets is essential for expanding the scope of data-driven scientific discovery. Recent advances in natural language processing (NLP) have facilitated automatic extraction of structured data from unstructured scientific literature. While existing approaches-multi-step and direct methods-offer valuable capabilities, they also come with limitations when applied independently. Here, we propose a novel hybrid text-mining framework that integrates the advantages of both methods to convert unstructured scientific text into structured data. Our approach first transforms raw text into entity-recognized text, and subsequently into structured form. Furthermore, beyond the overall data structuring framework, we also enhance entity recognition performance by introducing an entity marker-a simple yet effective technique that uses symbolic annotations to highlight target entities. Specifically, our entity marker-based hybrid approach not only consistently outperforms previous entity recognition approaches across three benchmark datasets (MatScholar, SOFC, and SOFC slot NER) but also improve the quality of final structured data-yielding up to a 58% improvement in entity-level F1 score and up to 83% improvement in relation-level F1 score compared to direct approach.
An empathic GPT-based chatbot to talk about mental disorders with Spanish teenagers
Authors: Alba María Mármol-Romero, Manuel García-Vega, Miguel Ángel García-Cumbreras, Arturo Montejo-Ráez
Date: 2025-05-09 00:00:00
Tasks: Chatbot, Language Modeling, Language Modelling

This paper presents a chatbot-based system to engage young Spanish people in the awareness of certain mental disorders through a self-disclosure technique. The study was carried out in a population of teenagers aged between 12 and 18 years. The dialogue engine mixes closed and open conversations, so certain controlled messages are sent to focus the chat on a specific disorder, which will change over time. Once a set of trial questions is answered, the system can initiate the conversation on the disorder under the focus according to the user's sensibility to that disorder, in an attempt to establish a more empathetic communication. Then, an open conversation based on the GPT-3 language model is initiated, allowing the user to express themselves with more freedom. The results show that these systems are of interest to young people and could help them become aware of certain mental disorders.
Tell Me Who Your Students Are: GPT Can Generate Valid Multiple-Choice Questions When Students' (Mis)Understanding Is Hinted
Authors: Machi Shimmei, Masaki Uto, Yuichiroh Matsubayashi, Kentaro Inui, Aditi Mallavarapu, Noboru Matsuda
Date: 2025-05-09 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Multiple-choice, Sentence, valid

The primary goal of this study is to develop and evaluate an innovative prompting technique, AnaQuest, for generating multiple-choice questions (MCQs) using a pre-trained large language model. In AnaQuest, the choice items are sentence-level assertions about complex concepts. The technique integrates formative and summative assessments. In the formative phase, students answer open-ended questions for target concepts in free text. For summative assessment, AnaQuest analyzes these responses to generate both correct and incorrect assertions. To evaluate the validity of the generated MCQs, Item Response Theory (IRT) was applied to compare item characteristics between MCQs generated by AnaQuest, a baseline ChatGPT prompt, and human-crafted items. An empirical study found that expert instructors rated MCQs generated by both AI models to be as valid as those created by human instructors. However, IRT-based analysis revealed that AnaQuest-generated questions - particularly those with incorrect assertions (foils) - more closely resembled human-crafted items in terms of difficulty and discrimination than those produced by ChatGPT.
TopicVD: A Topic-Based Dataset of Video-Guided Multimodal Machine Translation for Documentaries
Authors: Jinze Lv, Jian Chen, Zi Long, Xianghua Fu, Yin Chen
Date: 2025-05-09 00:00:00
Tasks: Domain Adaptation, Machine Translation, Multimodal Machine Translation, NMT, Translation

Most existing multimodal machine translation (MMT) datasets are predominantly composed of static images or short video clips, lacking extensive video data across diverse domains and topics. As a result, they fail to meet the demands of real-world MMT tasks, such as documentary translation. In this study, we developed TopicVD, a topic-based dataset for video-supported multimodal machine translation of documentaries, aiming to advance research in this field. We collected video-subtitle pairs from documentaries and categorized them into eight topics, such as economy and nature, to facilitate research on domain adaptation in video-guided MMT. Additionally, we preserved their contextual information to support research on leveraging the global context of documentaries in video-guided MMT. To better capture the shared semantics between text and video, we propose an MMT model based on a cross-modal bidirectional attention module. Extensive experiments on the TopicVD dataset demonstrate that visual information consistently improves the performance of the NMT model in documentary translation. However, the MMT model's performance significantly declines in out-of-domain scenarios, highlighting the need for effective domain adaptation methods. Additionally, experiments demonstrate that global context can effectively improve translation performance. % Dataset and our implementations are available at https://github.com/JinzeLv/TopicVD
Exploration of COVID-19 Discourse on Twitter: American Politician Edition
Authors: Cindy Kim, Daniela Puchall, Jiangyi Liang, Jiwon Kim
Date: 2025-05-08 00:00:00

The advent of the COVID-19 pandemic has undoubtedly affected the political scene worldwide and the introduction of new terminology and public opinions regarding the virus has further polarized partisan stances. Using a collection of tweets gathered from leading American political figures online (Republican and Democratic), we explored the partisan differences in approach, response, and attitude towards handling the international crisis. Implementation of the bag-of-words, bigram, and TF-IDF models was used to identify and analyze keywords, topics, and overall sentiments from each party. Results suggest that Democrats are more concerned with the casualties of the pandemic, and give more medical precautions and recommendations to the public whereas Republicans are more invested in political responsibilities such as keeping the public updated through media and carefully watching the progress of the virus. We propose a systematic approach to predict and distinguish a tweet's political stance (left or right leaning) based on its COVID-19 related terms using different classification algorithms on different language models.
KG-HTC: Integrating Knowledge Graphs into LLMs for Effective Zero-shot Hierarchical Text Classification
Authors: Qianbo Zang, Christophe Zgrzendek, Igor Tchappi, Afshin Khadangi, Johannes Sedlmeir
Date: 2025-05-08 00:00:00
Tasks: Knowledge Graphs, RAG, Retrieval-augmented Generation, text-classification, Text Classification

Hierarchical Text Classification (HTC) involves assigning documents to labels organized within a taxonomy. Most previous research on HTC has focused on supervised methods. However, in real-world scenarios, employing supervised HTC can be challenging due to a lack of annotated data. Moreover, HTC often faces issues with large label spaces and long-tail distributions. In this work, we present Knowledge Graphs for zero-shot Hierarchical Text Classification (KG-HTC), which aims to address these challenges of HTC in applications by integrating knowledge graphs with Large Language Models (LLMs) to provide structured semantic context during classification. Our method retrieves relevant subgraphs from knowledge graphs related to the input text using a Retrieval-Augmented Generation (RAG) approach. Our KG-HTC can enhance LLMs to understand label semantics at various hierarchy levels. We evaluate KG-HTC on three open-source HTC datasets: WoS, DBpedia, and Amazon. Our experimental results show that KG-HTC significantly outperforms three baselines in the strict zero-shot setting, particularly achieving substantial improvements at deeper levels of the hierarchy. This evaluation demonstrates the effectiveness of incorporating structured knowledge into LLMs to address HTC's challenges in large label spaces and long-tailed label distributions. Our code is available at: https://github.com/QianboZang/KG-HTC.
CogniSNN: A First Exploration to Random Graph Architecture based Spiking Neural Networks with Enhanced Expandability and Neuroplasticity
Authors: Yongsheng Huang, Peibo Duan, Zhipeng Liu, Kai Sun, Changsheng Zhang, Bin Zhang, Mingkun Xu
Date: 2025-05-09 00:00:00
Tasks: Continual Learning

Despite advances in spiking neural networks (SNNs) in numerous tasks, their architectures remain highly similar to traditional artificial neural networks (ANNs), restricting their ability to mimic natural connections between biological neurons. This paper develops a new modeling paradigm for SNN with random graph architecture (RGA), termed Cognition-aware SNN (CogniSNN). Furthermore, we improve the expandability and neuroplasticity of CogniSNN by introducing a modified spiking residual neural node (ResNode) to counteract network degradation in deeper graph pathways, as well as a critical path-based algorithm that enables CogniSNN to perform continual learning on new tasks leveraging the features of the data and the RGA learned in the old task. Experiments show that CogniSNN with re-designed ResNode performs outstandingly in neuromorphic datasets with fewer parameters, achieving 95.5% precision in the DVS-Gesture dataset with only 5 timesteps. The critical path-based approach decreases 3% to 5% forgetting while maintaining expected performance in learning new tasks that are similar to or distinct from the old ones. This study showcases the potential of RGA-based SNN and paves a new path for biologically inspired networks based on graph theory.
A High-Dimensional Feature Selection Algorithm Based on Multiobjective Differential Evolution
Authors: Zhenxing Zhang, Qianxiang An, Yilei Wang, Chenfeng Wu, Baoling Dong, Chunjie Zhou
Date: 2025-05-09 00:00:00
Tasks: Computational Efficiency, feature selection

Multiobjective feature selection seeks to determine the most discriminative feature subset by simultaneously optimizing two conflicting objectives: minimizing the number of selected features and the classification error rate. The goal is to enhance the model's predictive performance and computational efficiency. However, feature redundancy and interdependence in high-dimensional data present considerable obstacles to the search efficiency of optimization algorithms and the quality of the resulting solutions. To tackle these issues, we propose a high-dimensional feature selection algorithm based on multiobjective differential evolution. First, a population initialization strategy is designed by integrating feature weights and redundancy indices, where the population is divided into four subpopulations to improve the diversity and uniformity of the initial population. Then, a multiobjective selection mechanism is developed, in which feature weights guide the mutation process. The solution quality is further enhanced through nondominated sorting, with preference given to solutions with lower classification error, effectively balancing global exploration and local exploitation. Finally, an adaptive grid mechanism is applied in the objective space to identify densely populated regions and detect duplicated solutions. Experimental results on 11 UCI datasets of varying difficulty demonstrate that the proposed method significantly outperforms several state-of-the-art multiobjective feature selection approaches regarding feature selection performance.
Smart Starts: Accelerating Convergence through Uncommon Region Exploration
Authors: Xinyu Zhang, Mário Antunes, Tyler Estro, Erez Zadok, Klaus Mueller
Date: 2025-05-08 00:00:00
Tasks: Diversity

Initialization profoundly affects evolutionary algorithm (EA) efficacy by dictating search trajectories and convergence. This study introduces a hybrid initialization strategy combining empty-space search algorithm (ESA) and opposition-based learning (OBL). OBL initially generates a diverse population, subsequently augmented by ESA, which identifies under-explored regions. This synergy enhances population diversity, accelerates convergence, and improves EA performance on complex, high-dimensional optimization problems. Benchmark results demonstrate the proposed method's superiority in solution quality and convergence speed compared to conventional initialization techniques.
Economic Analysis and Optimization of Energy Storage Configuration for Park Power Systems Based on Random Forest and Genetic Algorithm
Authors: Yanghui Song, Aoqi Li, Lilei Huo
Date: 2025-05-07 00:00:00

This study aims to analyze the economic performance of various parks under different conditions, particularly focusing on the operational costs and power load balancing before and after the deployment of energy storage systems. Firstly, the economic performance of the parks without energy storage was analyzed using a random forest model. Taking Park A as an example, it was found that the cost had the greatest correlation with electricity purchase, followed by photovoltaic output, indicating that solar and wind power output are key factors affecting economic performance. Subsequently, the operation of the parks after the configuration of a 50kW/100kWh energy storage system was simulated, and the total cost and operation strategy of the energy storage system were calculated. The results showed that after the deployment of energy storage, the amount of wind and solar power curtailment in each park decreased, and the operational costs were reduced. Finally, a genetic algorithm was used to optimize the energy storage configuration of each park. The energy storage operation strategy was optimized through fitness functions, crossover operations, and mutation operations. After optimization, the economic indicators of Parks A, B, and C all improved. The research results indicate that by optimizing energy storage configuration, each park can reduce costs, enhance economic benefits, and achieve sustainable development of the power system.
Akkumula: Evidence accumulation driver models with Spiking Neural Networks
Authors: Alberto Morando
Date: 2025-04-30 00:00:00

Processes of evidence accumulation for motor control contribute to the ecological validity of driver models. According to established theories of cognition, drivers make control adjustments when a process of accumulation of perceptual inputs reaches a decision boundary. Unfortunately, there is not a standard way for building such models, limiting their use. Current implementations are hand-crafted, lack adaptability, and rely on inefficient optimization techniques that do not scale well with large datasets. This paper introduces Akkumula, an evidence accumulation modelling framework built using deep learning techniques to leverage established coding libraries, gradient optimization, and large batch training. The core of the library is based on Spiking Neural Networks, whose operation mimic the evidence accumulation process in the biological brain. The model was tested on data collected during a test-track experiment. Results are promising. The model fits well the time course of vehicle control (brake, accelerate, steering) based on vehicle sensor data. The perceptual inputs are extracted by a dedicated neural network, increasing the context-awareness of the model in dynamic scenarios. Akkumula integrates with existing machine learning architectures, benefits from continuous advancements in deep learning, efficiently processes large datasets, adapts to diverse driving scenarios, and maintains a degree of transparency in its core mechanisms.