Copertina

DS@GT at CheckThat! 2025: Detecting Subjectivity via Transfer-Learning and Corrective Data Augmentation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Heil, Maximilian, Bang, Dionne
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

DS@GT at CheckThat! 2025: Evaluating Context and Tokenization Strategies for Numerical Fact Verification
di: Heil, Maximilian, et al.
Pubblicazione: (2025)

DS@GT at CheckThat! 2025: Ensemble Methods for Detection of Scientific Discourse on Social Media
di: Parikh, Ayush, et al.
Pubblicazione: (2025)

DS@GT at CheckThat! 2025: Exploring Retrieval and Reranking Pipelines for Scientific Claim Source Retrieval on Social Media Discourse
di: Schofield, Jeanette, et al.
Pubblicazione: (2025)

The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Normalization, and Retrieval
di: Alam, Firoj, et al.
Pubblicazione: (2025)

HYBRINFOX at CheckThat! 2024 -- Task 2: Enriching BERT Models with the Expert System VAGO for Subjectivity Detection
di: Casanova, Morgane, et al.
Pubblicazione: (2024)

TurQUaz at CheckThat! 2025: Debating Large Language Models for Scientific Web Discourse Detection
di: Saraç, Tarık, et al.
Pubblicazione: (2025)

CEA-LIST at CheckThat! 2025: Evaluating LLMs as Detectors of Bias and Opinion in Text
di: Elbouanani, Akram, et al.
Pubblicazione: (2025)

UNH at CheckThat! 2025: Fine-tuning Vs Prompting in Claim Extraction
di: Wilder, Joe, et al.
Pubblicazione: (2025)

ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language Models for Verifying Numerical Claims
di: Anik, Anirban Saha, et al.
Pubblicazione: (2025)

DS@GT at CheckThat! 2025: A Simple Retrieval-First, LLM-Backed Framework for Claim Normalization
di: Pramov, Aleksandar, et al.
Pubblicazione: (2025)

HYBRINFOX at CheckThat! 2024 -- Task 1: Enhancing Language Models with Structured Information for Check-Worthiness Estimation
di: Faye, Géraud, et al.
Pubblicazione: (2024)

OpenFact at CheckThat! 2024: Combining Multiple Attack Methods for Effective Adversarial Text Generation
di: Lewoniewski, Włodzimierz, et al.
Pubblicazione: (2024)

IAI Group at CheckThat! 2024: Transformer Models and Data Augmentation for Checkworthy Claim Detection
di: Aarnes, Peter Røysland, et al.
Pubblicazione: (2024)

AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles
di: Fasulo, Matteo, et al.
Pubblicazione: (2025)

Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning
di: Wang, Kaipeng, et al.
Pubblicazione: (2024)

Improving Grammatical Error Correction via Contextual Data Augmentation
di: Wang, Yixuan, et al.
Pubblicazione: (2024)

XplaiNLP at CheckThat! 2025: Multilingual Subjectivity Detection with Finetuned Transformers and Prompt-Based Inference with Large Language Models
di: Sahitaj, Ariana, et al.
Pubblicazione: (2025)

QU-NLP at CheckThat! 2025: Multilingual Subjectivity in News Articles Detection using Feature-Augmented Transformer Models with Sequential Cross-Lingual Fine-Tuning
di: AL-Smadi, Mohammad
Pubblicazione: (2025)

Learning to Check: Unleashing Potentials for Self-Correction in Large Language Models
di: Zhang, Che, et al.
Pubblicazione: (2024)

Nullpointer at CheckThat! 2024: Identifying Subjectivity from Multilingual Text Sequence
di: Biswas, Md. Rafiul, et al.
Pubblicazione: (2024)

LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models
di: Tran, Hieu, et al.
Pubblicazione: (2024)

CORRECT: Context- and Reference-Augmented Reasoning and Prompting for Fact-Checking
di: Zhang, Delvin Ce, et al.
Pubblicazione: (2025)

A Reality Check on Context Utilisation for Retrieval-Augmented Generation
di: Hagström, Lovisa, et al.
Pubblicazione: (2024)

FactFinders at CheckThat! 2024: Refining Check-worthy Statement Detection with LLMs through Data Pruning
di: Li, Yufeng, et al.
Pubblicazione: (2024)

Breaking Data Efficiency Dilemma: A Federated and Augmented Learning Framework For Alzheimer's Disease Detection via Speech
di: Wei, Xiao, et al.
Pubblicazione: (2026)

Sanity Checks for Long-Form Hallucination Detection
di: Zollicoffer, Geigh, et al.
Pubblicazione: (2026)

RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models
di: An, Bang, et al.
Pubblicazione: (2025)

The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking
di: Struß, Julia Maria, et al.
Pubblicazione: (2026)

TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking
di: Hang, Ching Nam, et al.
Pubblicazione: (2025)

EdaCSC: Two Easy Data Augmentation Methods for Chinese Spelling Correction
di: Sheng, Lei, et al.
Pubblicazione: (2024)

WebDS: An End-to-End Benchmark for Web-based Data Science
di: Hsu, Ethan, et al.
Pubblicazione: (2025)

Beyond Correctness: Evaluating Subjective Writing Preferences Across Cultures
di: Ying, Shuangshuang, et al.
Pubblicazione: (2025)

Improving Context Fidelity via Native Retrieval-Augmented Reasoning
di: Wang, Suyuchen, et al.
Pubblicazione: (2025)

Pairing Analogy-Augmented Generation with Procedural Memory for Procedural Q&A
di: Roth, K, et al.
Pubblicazione: (2024)

Bolster Hallucination Detection via Prompt-Guided Data Augmentation
di: Li, Wenyun, et al.
Pubblicazione: (2025)

Scalable In-Context Learning on Tabular Data via Retrieval-Augmented Large Language Models
di: Wen, Xumeng, et al.
Pubblicazione: (2025)

Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs
di: Vladika, Juraj, et al.
Pubblicazione: (2025)

MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents
di: Tang, Liyan, et al.
Pubblicazione: (2024)

Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection
di: Nkhata, Gibson, et al.
Pubblicazione: (2025)

Conditional Semi-Supervised Data Augmentation for Spam Message Detection with Low Resource Data
di: Nuha, Ulin, et al.
Pubblicazione: (2024)