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FinNLP-FNP-LLMFinLegal-2025 Shared Task: Financial Misinformation Detection Challenge Task 9

FinNLP-FNP-LLMFinLegal-2025 Shared Task: Financial Misinform...

引用

Joint Workshop of the 9th Financial Technology and natural language processing, FinNLP 2025, the 6th Financial Narrative processing, FNP 2025, and the 1st Workshop on Large language Models for Finance and Legal, LLMFinLegal 2025, co-located with the 31st international conference on Computational Linguistics, COLING 2025

作者： Liu, Zhiwei Wang, Keyi Bao, Zhuo Zhang, Xin Dong, Jiping Yang, Kailai Kabir, Mohsinul Giannouris, Polydoros Xing, Rui Park, Seongchan Kim, Jaehong Li, Dong Xie, Qianqian Ananiadou, Sophia University of Manchester United Kingdom Artificial Intelligence Research Center Japan Columbia University United States Northwestern University United States Internet Domain Name System Beijing Engineering Research Center Co. China University of Chinese Academy of Sciences China University of Melbourne Australia Korea Republic of The FinAI United States Archimedes Athena RC Greece

ISBN: (纸本)9798891762091

Despite the promise of large language models (LLMs) in finance, their capabilities for financial misinformation detection (FMD) remain largely unexplored. To evaluate the capabilities of LLMs in FMD task, we introduce the financial misinformation detection shared task featured at COLING FinNLP-FNP-LLMFinLegal-2024, FMD Challenge. This challenge aims to evaluate the ability of LLMs to verify financial misinformation while generating plausible explanations. In this paper, we provide an overview of this task and dataset, summarize participants’ methods, and present their experimental evaluations, highlighting the effectiveness of LLMs in addressing the FMD task. To the best of our knowledge, the FMD Challenge is one of the first challenges for assessing LLMs in the field of FMD. Therefore, we provide detailed observations and draw conclusions for the future development of this field. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Large language Model Data Augmentation for Text-Pair Classification Tasks 24

Large Language Model Data Augmentation for Text-Pair Classif...

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13th international conference on Computing and Pattern Recognition, ICCPR 2024

作者： Li, Yuyang Zhang, Yuqing Du, Zelin Guo, Ziqi China University of Geosciences Beijing School of Information Engineering Beijing China

ISBN: (纸本)9798400717482

In recent years, large language models (LLMs) have demonstrated remarkable capabilities across various natural language processing tasks. This study explores the application of LLMs for data augmentation in text-pair classification tasks, such as semantic textual similarity and natural language inference. We propose a novel framework that leverages LLMs to generate diverse and contextually relevant paraphrases and transformations of text-pairs, enhancing the training data without manual annotation effort. Our experiments on widely-used benchmarks show that the augmented data not only improves model performance but also increases its robustness to out-of-domain examples. We perform extensive ablation studies to understand the contribution of different augmentation strategies and analyze the trade-offs between data diversity and noise. Additionally, we assess the generalization capabilities of models trained with augmented data across multiple architectures and dataset sizes. The results suggest that LLM-driven data augmentation is a promising approach to overcome data scarcity, reduce overfitting, and enhance the adaptability of text-pair classification systems. © 2024 Copyright held by the owner/author(s).

关键词： Data assimilation

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Convolutional Lie Operator for Sentence Classification 24

Convolutional Lie Operator for Sentence Classification

引用

8th international conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Rim, Daniela Noemi Choi, Heeyoul Department of Computer Science and Electrical Engineering Handong Global University Pohang Korea Republic of

ISBN: (纸本)9798400717383

Traditional Convolutional Neural Networks have been successful in capturing local, position-invariant features in text, but their capacity to model complex transformation within language can be further explored. In this work, we explore a novel approach by integrating Lie Convolutions into Convolutional-based sentence classifiers, inspired by the ability of Lie group operations to capture complex, non-Euclidean symmetries. Our proposed models SCLie and DPCLie empirically outperform traditional Convolutional-based sentence classifiers, suggesting that Lie-based models relatively improve the accuracy by capturing transformations not commonly associated with language. Our findings motivate more exploration of new paradigms in language modeling. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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DKD2L: Dual knowledge Distillation Dynamic Learning for sketch-based 3D shape retrieval

DKD2L: Dual Knowledge Distillation Dynamic Learning for sket...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Su, Yawen Bai, Jing Lin, Gan School of Computer Science and Engineering North Minzu University Yinchuan China The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission China

ISBN: (纸本)9798350368741

Sketch-based 3D shape retrieval has become a prominent area of research in computer vision, confronting challenges related to the inherent diversity and abstraction of sketches, as well as inter-domain discrepancies. This paper introduces a novel approach called Dual knowledge Distillation Dynamic Learning (DKD2L) aimed at enhancing the extraction of spatio-temporal features for sketch-based 3D shape retrieval. We develop a temporal feature extraction network to effectively capture the dynamic temporal characteristics of sketches and improve retrieval efficiency through temporal knowledge distillation. Additionally, to tackle intra-class variation and inter-class imbalance, we apply semantic knowledge distillation, enabling the 3D shape network to guide the sketch network in capturing common semantic information. This approach facilitates precise cross-modal alignment and enhances retrieval accuracy. Extensive experiments on two benchmark datasets demonstrate that DKD2L surpasses existing state-of-the-art methods. © 2025 IEEE.

关键词： 3D Shape Retrieval knowledge Distillation Sketch-based 3D Shape Retrieval Temporal Feature

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SetBERT: Enhancing Retrieval Performance for Boolean Logic and Set Operation Queries 24

SetBERT: Enhancing Retrieval Performance for Boolean Logic a...

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8th international conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Mai, Quan Gauch, Susan Adams, Douglas Department of Electrical Engineering and Computer Science University of Arkansas FayettevilleAR United States Department of Sociology and Criminology University of Arkansas FayettevilleAR United States

ISBN: (纸本)9798400717383

We introduce SetBERT, a fine-tuned BERT-based model designed to enhance query embeddings for set operations and Boolean logic queries, such as Intersection (AND), Difference (NOT), and Union (OR). SetBERT significantly improves retrieval performance for logic-structured queries, an area where both traditional and neural retrieval methods typically underperform. We propose an innovative use of inversed-contrastive loss, focusing on identifying the negative sentence, and fine-tuning BERT with a dataset generated via prompt GPT. Furthermore, we demonstrate that, unlike other BERT-based models, fine-tuning with triplet loss actually degrades performance for this specific task. Our experiments reveal that SetBERT-base not only significantly outperforms BERT-base (up to a 63% improvement in Recall) but also achieves performance comparable to the much larger BERT-large model, despite being only one-third the size. © 2024 Copyright held by the owner/author(s).

关键词： Content based retrieval

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Artificial Intelligence (AI) Assisted Literature Review 3

Artificial Intelligence (AI) Assisted Literature Review

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Hilda, Jabanjalin Jeevaa, S.V. Bhadhrinath, U. Solanki, Darshini Sr. School of Computer Science Engineering Vellore Institute of Technology Vellore India Vellore Institue of Tehnology Vellore Vellore India

ISBN: (纸本)9798331527549

This research aims to address the inefficiencies and inaccuracies in traditional literature review methodologies by integrating advanced Artificial Intelligence (AI) technologies, specifically transformer-based models. The study focuses on optimizing PDF-to-text conversion through image and character recognition, thereby minimizing resource wastage and errors associated with manual transcription. Additionally, the research employs advanced natural language processing (NLP) techniques to automate the identification of critical phrases such as keywords and organizational entities, significantly reducing manual errors and improving accuracy. The methodology also includes the use of sophisticated summarization algorithms and sentiment analysis, streamlining the process of summarizing extensive literature and identifying study limitations. By leveraging state-of-the-art transformer models and industry-standard libraries, this research introduces a novel, efficient framework for literature analysis. The outcome is a comprehensive, AI-powered toolkit that enhances the speed, accuracy, and precision of literature reviews, ultimately providing researchers with a more reliable and resource-efficient approach to navigating scholarly content. © 2025 IEEE.

关键词： natural language processing systems

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Validating Semantic Artifacts with Large language Models

Validating Semantic Artifacts with Large Language Models

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21st international conference on The Semantic Web (ESWC)

作者： Tufek, Nilay Thuluva, Aparna Saissre Just, Valentin Philipp Ekaputra, Fajar J. Bandyopadhyay, Tathagata Sabou, Marta Hanbury, Allan Siemens AG Dept Technol Munich Germany TU Wien Inst Comp Engn Vienna Austria WU Vienna Inst Data Proc & Knowledge Management Vienna Austria TU Wien Inst Informat Syst Engn Vienna Austria

ISBN: (纸本)9783031789519;9783031789526

As part of knowledge engineering workflows, semantic artifacts, such as ontologies, knowledge graphs or semantic descriptions based on industrial standards, are often validated in terms of their compliance with requirements expressed in natural language (e.g., ontology competency questions, standard specifications). Key to this process is the translation of the requirements in machine-actionable queries (e.g., SPARQL) that can automate the validation process. This manual translation process is time-consuming, error-prone and challenging, especially in areas where domain experts might lack knowledge of semantic technologies. In this paper, we propose a Large language Models (LLMs) based approach to translate requirements texts into SPARQL queries and test it in validation use cases related to SAREF and OPC UA Robotics. F1 scores of 88-100% indicate the feasibility of the approach and its potential impact on ensuring high quality semantic artifacts and further uptake of the semantic technologies (industrial) domains.

关键词： Semantic Artifacts Validation LLM OPC UA

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A Recap into the Application of natural language processing for Identification of Cognitive Impairment in Patients 1

A Recap into the Application of Natural Language Processing ...

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1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Ghorpade-Aher, Jayshree Desai, Urvi MIT World Peace University Department of Computer Engineering and Technology Pune India

ISBN: (纸本)9798350355611

Diseases and disorders causing cognitive decline reduce the quality of life of their victims drastically by hindering them from performing basic everyday tasks normally. The earlier a patient can be diagnosed and begin treatment, the better the prognosis tends to be and the application of technology in diagnostics has sped up the process considerably. Artificial Intelligence, Machine Learning and now Deep Learning are being used to aid doctors and diagnosticians in the swifter, more streamlined diagnosis of their patients. It has been observed that changes in the speech patterns of patients show up as a symptom early in the process of their declining cognition. This makes natural language processing(NLP) a powerful tool to execute the early detection of psychological and neurological impairments using safer, less expensive means. Several approaches being applied to further this research include lexico-syntactic and acoustic analysis as well as tools like BERT transformer and LASSO Regression. Classification is attempted using Machine Learning models like Random Forest, Gradient Boosting or Support Vector Machine as well as neural networks. This studies the merits, demerits and novelties of some such studies and expands on the technology used therein. Still in the nascent stages of development, the application of NLP to diagnose and categorize cognitive decline face many hurdles such as limited access to data-sets, lack of explainability leading to low usability in real world scenarios and lower accuracy than is achieved by Machine Learning and Deep Learning. However, what little research does exist points to a promising future where cheaper, earlier and non-invasive diagnosis would be a reality for patients of cognitive decline. © 2025 IEEE.

关键词： Deep learning

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Human Evaluation of Procedural knowledge Graph Extraction from Text with Large language Models 24th

Human Evaluation of Procedural Knowledge Graph Extraction f...

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24th international conference on knowledge engineering and knowledge Management, EKAW 2024

作者： Carriero, Valentina Anita Azzini, Antonia Baroni, Ilaria Scrocca, Mario Celino, Irene Cefriel – Politecnico di Milano viale Sarca 226 Milano20126 Italy

ISBN: (纸本)9783031777912

Procedural knowledge is the know-how expressed in the form of sequences of steps needed to perform some tasks. Procedures are usually described by means of natural language texts, such as recipes or maintenance manuals, possibly spread across different documents and systems, and their interpretation and subsequent execution is often left to the reader. Representing such procedures in a knowledge Graph (KG) can be the basis to build digital tools to support those users who need to apply or execute them. In this paper, we leverage Large language Model (LLM) capabilities and propose a prompt engineering approach to extract steps, actions, objects, equipment and temporal information from a textual procedure, in order to populate a Procedural KG according to a pre-defined ontology. We evaluate the KG extraction results by means of a user study, in order to qualitatively and quantitatively assess the perceived quality and usefulness of the LLM-extracted procedural knowledge. We show that LLMs can produce outputs of acceptable quality and we assess the subjective perception of AI by human evaluators. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： knowledge graph

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Sample Efficient Reinforcement Learning via Large Vision language Model Distillation

Sample Efficient Reinforcement Learning via Large Vision Lan...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Lee, Donghoon Luu, Tung M. Lee, Younghwan Yoo, Chang D. Robotics Program KAIST Daejeon Korea Republic of Electrical Engineering KAIST Daejeon Korea Republic of

ISBN: (纸本)9798350368741

Recent research highlights the potential of multimodal foundation models in tackling complex decision-making challenges. However, their large parameters make real-world deployment resource-intensive and often impractical for constrained systems. Reinforcement learning (RL) shows promise for task-specific agents but suffers from high sample complexity, limiting practical applications. To address these challenges, we introduce LVLM to Policy (LVLM2P), a novel framework that distills knowledge from large vision-language models (LVLM) into more efficient RL agents. Our approach leverages the LVLM as a teacher, providing instructional actions based on trajectories collected by the RL agent, which helps reduce less meaningful exploration in the early stages of learning, thereby significantly accelerating the agent's learning progress. Additionally, by leveraging the LVLM to suggest actions directly from visual observations, we eliminate the need for manual textual descriptors of the environment, enhancing applicability across diverse tasks. Experiments show that LVLM2P significantly enhances the sample efficiency of baseline RL algorithms. The code is available at https://***/i22024/LVLM2P. © 2025 IEEE.

关键词： knowledge Distillation Reinforcement Learning Vision language Model

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