:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Nimase, Ojas, Hong, Sanghyun
Format:	Preprint
Publié:	2024
Sujets:	Computation and Language Machine Learning
Accès en ligne:	https://arxiv.org/abs/2403.12469
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?
par: Nimase, Ojas, et autres
Publié: (2026)

An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore Framework
par: Qin, Zhenkai, et autres
Publié: (2024)

SemIRNet: A Semantic Irony Recognition Network for Multimodal Sarcasm Detection
par: Zhou, Jingxuan, et autres
Publié: (2025)

LeaPformer: Enabling Linear Transformers for Autoregressive and Simultaneous Tasks via Learned Proportions
par: Agostinelli, Victor, et autres
Publié: (2024)

On Sarcasm Detection with OpenAI GPT-based Models
par: Gole, Montgomery, et autres
Publié: (2023)

Sarcasm Detection in Tweets with BERT and GloVe Embeddings
par: Khatri, Akshay, et autres
Publié: (2020)

When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models
par: Zheng, Mingqian, et autres
Publié: (2023)

Sarcasm Detection on Reddit Using Classical Machine Learning and Feature Engineering
par: Karmaker, Subrata
Publié: (2025)

LUDOBENCH: Evaluating LLM Behavioural Decision-Making Through Spot-Based Board Game Scenarios in Ludo
par: Jain, Ojas, et autres
Publié: (2026)

Transfer Learning via Lexical Relatedness: A Sarcasm and Hate Speech Case Study
par: Cabrera, Angelly, et autres
Publié: (2025)

On the Impact of Language Nuances on Sentiment Analysis with Large Language Models: Paraphrasing, Sarcasm, and Emojis
par: Bhargava, Naman, et autres
Publié: (2025)

MUStReason: A Benchmark for Diagnosing Pragmatic Reasoning in Video-LMs for Multimodal Sarcasm Detection
par: Saha, Anisha, et autres
Publié: (2025)

When Does Removing LayerNorm Help? Activation Bounding as a Regime-Dependent Implicit Regularizer
par: Verma, Lucky
Publié: (2026)

Context-Free Recognition with Transformers
par: Jerad, Selim, et autres
Publié: (2026)

When Do LLMs Reason? A Dynamical Systems View via Entropy Phase Transitions
par: Xia, Wei, et autres
Publié: (2026)

Sarcasm Detection in a Less-Resourced Language
par: Đoković, Lazar, et autres
Publié: (2024)

When More is Less: Understanding Chain-of-Thought Length in LLMs
par: Wu, Yuyang, et autres
Publié: (2025)

Effective Context Modeling Framework for Emotion Recognition in Conversations
par: Van, Cuong Tran, et autres
Publié: (2024)

Learning When to Attend: Conditional Memory Access for Long-Context LLMs
par: Choudhary, Sakshi, et autres
Publié: (2026)

When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations
par: Petrov, Aleksandar, et autres
Publié: (2023)

Context Biasing for Pronunciation-Orthography Mismatch in Automatic Speech Recognition
par: Huber, Christian, et autres
Publié: (2025)

What Do Language Models Learn in Context? The Structured Task Hypothesis
par: Li, Jiaoda, et autres
Publié: (2024)

When Models Know More Than They Say: Probing Analogical Reasoning in LLMs
par: McGovern, Hope, et autres
Publié: (2026)

From Unstructured Data to In-Context Learning: Exploring What Tasks Can Be Learned and When
par: Wibisono, Kevin Christian, et autres
Publié: (2024)

Beyond Long Context: When Semantics Matter More than Tokens
par: Chawdhury, Tarun Kumar, et autres
Publié: (2025)

When Does Divide and Conquer Work for Long Context LLM? A Noise Decomposition Framework
par: Xu, Zhen, et autres
Publié: (2025)

Do pretrained Transformers Learn In-Context by Gradient Descent?
par: Shen, Lingfeng, et autres
Publié: (2023)

Multi-Response Preference Optimization with Augmented Ranking Dataset
par: Gwon, Hansle, et autres
Publié: (2024)

Investigating the Pre-Training Dynamics of In-Context Learning: Task Recognition vs. Task Learning
par: Wang, Xiaolei, et autres
Publié: (2024)

When Does Metadata Conditioning (NOT) Work for Language Model Pre-Training? A Study with Context-Free Grammars
par: Higuchi, Rei, et autres
Publié: (2025)

When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs
par: Jeong, Soyeong, et autres
Publié: (2025)

Rethinking the Unsolvable: When In-Context Search Meets Test-Time Scaling
par: Xia, Fanzeng, et autres
Publié: (2025)

More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives
par: Zhang, Xiaoqing, et autres
Publié: (2025)

Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation
par: Zhang, Yuhui, et autres
Publié: (2023)

From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?
par: Mueller, Aaron, et autres
Publié: (2025)

When Refusals Fail: Unstable Safety Mechanisms in Long-Context LLM Agents
par: Hadeliya, Tsimur, et autres
Publié: (2025)

More Edits, More Stable: Understanding the Lifelong Normalization in Sequential Model Editing
par: Ma, Xin, et autres
Publié: (2026)

HINT: Helping Ineffective Rollouts Navigate Towards Effectiveness
par: Wang, Xinyi, et autres
Publié: (2025)

When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models
par: You, Haoran, et autres
Publié: (2024)

In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering
par: Liu, Sheng, et autres
Publié: (2023)