Saved in:
| Main Authors: | Honda, Ukyo, Oka, Tatsushi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.02378 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Not Eliminate but Aggregate: Post-Hoc Control over Mixture-of-Experts to Address Shortcut Shifts in Natural Language Understanding
by: Honda, Ukyo, et al.
Published: (2024)
by: Honda, Ukyo, et al.
Published: (2024)
Annotation-Efficient Language Model Alignment via Diverse and Representative Response Texts
by: Jinnai, Yuu, et al.
Published: (2024)
by: Jinnai, Yuu, et al.
Published: (2024)
Revisiting the Capacity Gap in Chain-of-Thought Distillation from a Practical Perspective
by: Kajitsuka, Tokio, et al.
Published: (2026)
by: Kajitsuka, Tokio, et al.
Published: (2026)
A Single Linear Layer Yields Task-Adapted Low-Rank Matrices
by: Kim, Hwichan, et al.
Published: (2024)
by: Kim, Hwichan, et al.
Published: (2024)
On the True Distribution Approximation of Minimum Bayes-Risk Decoding
by: Ohashi, Atsumoto, et al.
Published: (2024)
by: Ohashi, Atsumoto, et al.
Published: (2024)
Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding
by: Jinnai, Yuu, et al.
Published: (2024)
by: Jinnai, Yuu, et al.
Published: (2024)
Exploring the Robustness of In-Context Learning with Noisy Labels
by: Cheng, Chen, et al.
Published: (2024)
by: Cheng, Chen, et al.
Published: (2024)
Model-Based Minimum Bayes Risk Decoding for Text Generation
by: Jinnai, Yuu, et al.
Published: (2023)
by: Jinnai, Yuu, et al.
Published: (2023)
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
by: Zhang, Qizheng, et al.
Published: (2025)
by: Zhang, Qizheng, et al.
Published: (2025)
Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales
by: Resck, Lucas E., et al.
Published: (2024)
by: Resck, Lucas E., et al.
Published: (2024)
Is In-Context Learning Learning?
by: de Wynter, Adrian
Published: (2025)
by: de Wynter, Adrian
Published: (2025)
Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning
by: Chen, Yanda, et al.
Published: (2024)
by: Chen, Yanda, et al.
Published: (2024)
Improving Context-Aware Preference Modeling for Language Models
by: Pitis, Silviu, et al.
Published: (2024)
by: Pitis, Silviu, et al.
Published: (2024)
On the Robustness of Transformers against Context Hijacking for Linear Classification
by: Li, Tianle, et al.
Published: (2025)
by: Li, Tianle, et al.
Published: (2025)
Exploring and Improving Drafts in Blockwise Parallel Decoding
by: Kim, Taehyeon, et al.
Published: (2024)
by: Kim, Taehyeon, et al.
Published: (2024)
Memorization in In-Context Learning
by: Golchin, Shahriar, et al.
Published: (2024)
by: Golchin, Shahriar, et al.
Published: (2024)
Revisiting In-Context Learning with Long Context Language Models
by: Baek, Jinheon, et al.
Published: (2024)
by: Baek, Jinheon, et al.
Published: (2024)
Context-Enhanced Contrastive Search for Improved LLM Text Generation
by: Sen, Jaydip, et al.
Published: (2025)
by: Sen, Jaydip, et al.
Published: (2025)
Structured Document Translation via Format Reinforcement Learning
by: Song, Haiyue, et al.
Published: (2025)
by: Song, Haiyue, et al.
Published: (2025)
When and How Unlabeled Data Provably Improve In-Context Learning
by: Li, Yingcong, et al.
Published: (2025)
by: Li, Yingcong, et al.
Published: (2025)
ICLR: In-Context Learning of Representations
by: Park, Core Francisco, et al.
Published: (2024)
by: Park, Core Francisco, et al.
Published: (2024)
Many-Shot In-Context Learning
by: Agarwal, Rishabh, et al.
Published: (2024)
by: Agarwal, Rishabh, et al.
Published: (2024)
In-Context Learning Learns Label Relationships but Is Not Conventional Learning
by: Kossen, Jannik, et al.
Published: (2023)
by: Kossen, Jannik, et al.
Published: (2023)
Clarify: Improving Model Robustness With Natural Language Corrections
by: Lee, Yoonho, et al.
Published: (2024)
by: Lee, Yoonho, et al.
Published: (2024)
Robust Explanations for User Trust in Enterprise NLP Systems
by: Zhang, Guilin, et al.
Published: (2026)
by: Zhang, Guilin, et al.
Published: (2026)
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization
by: Hsieh, Cheng-Yu, et al.
Published: (2024)
by: Hsieh, Cheng-Yu, et al.
Published: (2024)
Rapid Word Learning Through Meta In-Context Learning
by: Wang, Wentao, et al.
Published: (2025)
by: Wang, Wentao, et al.
Published: (2025)
Exploring Domain Robust Lightweight Reward Models based on Router Mechanism
by: Namgoong, Hyuk, et al.
Published: (2024)
by: Namgoong, Hyuk, et al.
Published: (2024)
Robustly Improving LLM Fairness in Realistic Settings via Interpretability
by: Karvonen, Adam, et al.
Published: (2025)
by: Karvonen, Adam, et al.
Published: (2025)
How Causal Abstraction Underpins Computational Explanation
by: Geiger, Atticus, et al.
Published: (2025)
by: Geiger, Atticus, et al.
Published: (2025)
Estimation of Concept Explanations Should be Uncertainty Aware
by: Piratla, Vihari, et al.
Published: (2023)
by: Piratla, Vihari, et al.
Published: (2023)
CoSy: Evaluating Textual Explanations of Neurons
by: Kopf, Laura, et al.
Published: (2024)
by: Kopf, Laura, et al.
Published: (2024)
Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning
by: Liu, Hui, et al.
Published: (2024)
by: Liu, Hui, et al.
Published: (2024)
Which Attention Heads Matter for In-Context Learning?
by: Yin, Kayo, et al.
Published: (2025)
by: Yin, Kayo, et al.
Published: (2025)
Learning and Enforcing Context-Sensitive Control for LLMs
by: Albinhassan, Mohammad, et al.
Published: (2026)
by: Albinhassan, Mohammad, et al.
Published: (2026)
DemoShapley: Valuation of Demonstrations for In-Context Learning
by: Xie, Shan, et al.
Published: (2024)
by: Xie, Shan, et al.
Published: (2024)
LLoCO: Learning Long Contexts Offline
by: Tan, Sijun, et al.
Published: (2024)
by: Tan, Sijun, et al.
Published: (2024)
In-Context Learning Dynamics with Random Binary Sequences
by: Bigelow, Eric J., et al.
Published: (2023)
by: Bigelow, Eric J., et al.
Published: (2023)
Is In-Context Learning Sufficient for Instruction Following in LLMs?
by: Zhao, Hao, et al.
Published: (2024)
by: Zhao, Hao, et al.
Published: (2024)
Inference and Verbalization Functions During In-Context Learning
by: Tao, Junyi, et al.
Published: (2024)
by: Tao, Junyi, et al.
Published: (2024)
Similar Items
-
Not Eliminate but Aggregate: Post-Hoc Control over Mixture-of-Experts to Address Shortcut Shifts in Natural Language Understanding
by: Honda, Ukyo, et al.
Published: (2024) -
Annotation-Efficient Language Model Alignment via Diverse and Representative Response Texts
by: Jinnai, Yuu, et al.
Published: (2024) -
Revisiting the Capacity Gap in Chain-of-Thought Distillation from a Practical Perspective
by: Kajitsuka, Tokio, et al.
Published: (2026) -
A Single Linear Layer Yields Task-Adapted Low-Rank Matrices
by: Kim, Hwichan, et al.
Published: (2024) -
On the True Distribution Approximation of Minimum Bayes-Risk Decoding
by: Ohashi, Atsumoto, et al.
Published: (2024)