:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Honda, Ukyo, Murakami, Soichiro, Zhang, Peinan
Format:	Preprint
Publié:	2025
Sujets:	Computation and Language
Accès en ligne:	https://arxiv.org/abs/2509.20820
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

FaithCAMERA: Construction of a Faithful Dataset for Ad Text Generation
par: Kato, Akihiko, et autres
Publié: (2024)

Does Self-Consistency Improve the Recall of Encyclopedic Knowledge?
par: Hoshino, Sho, et autres
Publié: (2026)

Exploring the Relationship Between Diversity and Quality in Ad Text Generation
par: Aoki, Yoichi, et autres
Publié: (2025)

Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding
par: Jinnai, Yuu, et autres
Publié: (2024)

Not Eliminate but Aggregate: Post-Hoc Control over Mixture-of-Experts to Address Shortcut Shifts in Natural Language Understanding
par: Honda, Ukyo, et autres
Publié: (2024)

Exploring Explanations Improves the Robustness of In-Context Learning
par: Honda, Ukyo, et autres
Publié: (2025)

Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation
par: Mita, Masato, et autres
Publié: (2023)

Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity
par: Hoshino, Sho, et autres
Publié: (2024)

Routing by Analogy: kNN-Augmented Expert Assignment for Mixture-of-Experts
par: Lyu, Boxuan, et autres
Publié: (2026)

Revisiting the Capacity Gap in Chain-of-Thought Distillation from a Practical Perspective
par: Kajitsuka, Tokio, et autres
Publié: (2026)

Toward LLMs Beyond English-Centric Development
par: Takase, Sho, et autres
Publié: (2026)

AdParaphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Dataset
par: Murakami, Soichiro, et autres
Publié: (2025)

AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Texts
par: Murakami, Soichiro, et autres
Publié: (2025)

Annotation-Efficient Language Model Alignment via Diverse and Representative Response Texts
par: Jinnai, Yuu, et autres
Publié: (2024)

Reinforcement Learning for Edit-Based Non-Autoregressive Neural Machine Translation
par: Wang, Hao, et autres
Publié: (2024)

On the True Distribution Approximation of Minimum Bayes-Risk Decoding
par: Ohashi, Atsumoto, et autres
Publié: (2024)

Many-Shot In-Context Learning
par: Agarwal, Rishabh, et autres
Publié: (2024)

A Single Linear Layer Yields Task-Adapted Low-Rank Matrices
par: Kim, Hwichan, et autres
Publié: (2024)

On Many-Shot In-Context Learning for Long-Context Evaluation
par: Zou, Kaijian, et autres
Publié: (2024)

Model-Based Minimum Bayes Risk Decoding for Text Generation
par: Jinnai, Yuu, et autres
Publié: (2023)

Efficient Many-Shot In-Context Learning with Dynamic Block-Sparse Attention
par: Xiao, Emily, et autres
Publié: (2025)

Selecting Demonstrations for Many-Shot In-Context Learning via Gradient Matching
par: Zhang, Jianfei, et autres
Publié: (2025)

Many-Shot In-Context Learning for Molecular Inverse Design
par: Moayedpour, Saeed, et autres
Publié: (2024)

Out-of-the-Box Conditional Text Embeddings from Large Language Models
par: Yamada, Kosuke, et autres
Publié: (2025)

GAIN: A Benchmark for Goal-Aligned Decision-Making of Large Language Models under Imperfect Norms
par: Kawarada, Masayuki, et autres
Publié: (2026)

Towards Compute-Optimal Many-Shot In-Context Learning
par: Golchin, Shahriar, et autres
Publié: (2025)

An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages
par: Lu, Yinhan, et autres
Publié: (2026)

Many-Shot In-Context Learning in Multimodal Foundation Models
par: Jiang, Yixing, et autres
Publié: (2024)

In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning
par: Duan, Yifei, et autres
Publié: (2024)

Scaling Laws for Many-Shot In-Context Learning with Self-Generated Annotations
par: Gu, Zhengyao, et autres
Publié: (2025)

Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study
par: Song, Mingyang, et autres
Publié: (2024)

Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning
par: Huang, Brandon, et autres
Publié: (2024)

CaughtCheating: Is Your MLLM a Good Cheating Detective? Exploring the Boundary of Visual Perception and Reasoning
par: Li, Ming, 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)

Oogiri-Master: Benchmarking Humor Understanding via Oogiri
par: Murakami, Soichiro, et autres
Publié: (2025)

Who Laughs with Whom? Disentangling Influential Factors in Humor Preferences across User Clusters and LLMs
par: Murakami, Soichiro, et autres
Publié: (2026)

Many-Shot Regurgitation (MSR) Prompting
par: Sonkar, Shashank, et autres
Publié: (2024)

Improving In-Context Learning with Reasoning Distillation
par: Sadeq, Nafis, et autres
Publié: (2025)

What Really Matters in Many-Shot Attacks? An Empirical Study of Long-Context Vulnerabilities in LLMs
par: Kim, Sangyeop, et autres
Publié: (2025)

In-Context Learning for Few-Shot Nested Named Entity Recognition
par: Zhang, Meishan, et autres
Publié: (2024)