Copertina

Beyond One-Size-Fits-All: Personalized Harmful Content Detection with In-Context Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhang, Rufan, Zhang, Lin, Mi, Xianghang
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts
di: Hong, Hanhua, et al.
Pubblicazione: (2025)

One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction
di: Liu, Ben, et al.
Pubblicazione: (2025)

Beyond One-Size-Fits-All Summarization: Customizing Summaries for Diverse Users
di: Duran, Mehmet Samet, et al.
Pubblicazione: (2025)

Theorem Provers: One Size Fits All?
di: Oates, Harrison, et al.
Pubblicazione: (2025)

Beyond One-Size-Fits-All Pruning via Evolutionary Metric Search for Large Language Models
di: Liu, Shuqi, et al.
Pubblicazione: (2025)

Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agents
di: Yu, Jianxiang, et al.
Pubblicazione: (2026)

Beyond One-Size-Fits-All: Multi-Domain, Multi-Task Framework for Embedding Model Selection
di: Khetan, Vivek
Pubblicazione: (2024)

One Size Does Not Fit All: Token-Wise Adaptive Compression for KV Cache
di: Lu, Liming, et al.
Pubblicazione: (2026)

One-Topic-Doesn't-Fit-All: Transcreating Reading Comprehension Test for Personalized Learning
di: Han, Jieun, et al.
Pubblicazione: (2025)

SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection
di: Li, Yekai, et al.
Pubblicazione: (2024)

No One Size Fits All: QueryBandits for Hallucination Mitigation
di: Cho, Nicole, et al.
Pubblicazione: (2026)

Beyond Accuracy: An Explainability-Driven Analysis of Harmful Content Detection
di: Dhara, Trishita, et al.
Pubblicazione: (2026)

"One-Size-Fits-All"? Examining Expectations around What Constitute "Fair" or "Good" NLG System Behaviors
di: Lucy, Li, et al.
Pubblicazione: (2023)

Target Span Detection for Implicit Harmful Content
di: Jafari, Nazanin, et al.
Pubblicazione: (2024)

One Size Does Not Fit All: A Distribution-Aware Sparsification for More Precise Model Merging
di: Luo, Yingfeng, et al.
Pubblicazione: (2025)

No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs
di: Wu, Wei-Chi, et al.
Pubblicazione: (2026)

One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction
di: Lu, Yuxing, et al.
Pubblicazione: (2026)

Harmful Suicide Content Detection
di: Park, Kyumin, et al.
Pubblicazione: (2024)

Are LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-Tuning
di: Maggini, Michele Joshua, et al.
Pubblicazione: (2025)

LLM-based Semantic Augmentation for Harmful Content Detection
di: Meguellati, Elyas, et al.
Pubblicazione: (2025)

One Size Does Not Fit All: Exploring Variable Thresholds for Distance-Based Multi-Label Text Classification
di: Van Nooten, Jens, et al.
Pubblicazione: (2025)

When Youth Enter the Algorithmic Wild: Discovering and Understanding Potentially Harmful Teen Videos on Douyin and Kwai
di: Zhou, Shaoxuan, et al.
Pubblicazione: (2026)

ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark
di: Liu, Kangwei, et al.
Pubblicazione: (2025)

Beyond One-Size-Fits-All: A Survey of Personalized Affective Computing in Human-Agent Interaction
di: Li, Jialin, et al.
Pubblicazione: (2023)

Beyond One-Size-Fits-All Exercises: Personalizing Computer Science Worksheets with Large Language Models
di: Ortiz, Franco, et al.
Pubblicazione: (2026)

One Size Fits None: Heuristic Collapse in LLM Investment Advice
di: Ross, Jillian, et al.
Pubblicazione: (2026)

Towards Comprehensive Detection of Chinese Harmful Memes
di: Lu, Junyu, et al.
Pubblicazione: (2024)

No One-Size-Fits-All: Building Systems For Translation to Bashkir, Kazakh, Kyrgyz, Tatar and Chuvash Using Synthetic And Original Data
di: Karpov, Dmitry
Pubblicazione: (2026)

DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects
di: Lucas, Jason, et al.
Pubblicazione: (2026)

Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases
di: Huang, Wenhao, et al.
Pubblicazione: (2024)

Learning-to-Context Slope: Evaluating In-Context Learning Effectiveness Beyond Performance Illusions
di: Wang, Dingzriui, et al.
Pubblicazione: (2025)

Beyond Static Benchmarks: Synthesizing Harmful Content via Persona-based Simulation for Robust Evaluation
di: Lee, Huije, et al.
Pubblicazione: (2026)

Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM
di: Zhang, Chi, et al.
Pubblicazione: (2025)

KID: Knowledge-Injected Dual-Head Learning for Knowledge-Grounded Harmful Meme Detection
di: Li, Yaocong, et al.
Pubblicazione: (2026)

Seeing the Unseen: Rethinking Illicit Promotion Detection with In-Context Learning
di: Wu, Sangyi, et al.
Pubblicazione: (2026)

Demonstrations Are All You Need: Advancing Offensive Content Paraphrasing using In-Context Learning
di: Som, Anirudh, et al.
Pubblicazione: (2023)

The Hidden Language of Harm: Examining the Role of Emojis in Harmful Online Communication and Content Moderation
di: Zhou, Yuhang, et al.
Pubblicazione: (2025)

Look Before You Leap: Enhancing Attention and Vigilance Regarding Harmful Content with GuidelineLLM
di: Zhang, Shaoqing, et al.
Pubblicazione: (2024)

From Judgment to Interference: Early Stopping LLM Harmful Outputs via Streaming Content Monitoring
di: Li, Yang, et al.
Pubblicazione: (2025)

One Jump Is All You Need: Short-Cutting Transformers for Early Exit Prediction with One Jump to Fit All Exit Levels
di: Seshadri, Amrit Diggavi
Pubblicazione: (2025)