Rectifying Demonstration Shortcut in In-Context Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Jang, Joonwon, Jang, Sanghwan, Kweon, Wonbin, Jeon, Minjin, Yu, Hwanjo |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Verbosity-Aware Rationale Reduction: Effective Reduction of Redundant Rationale via Principled Criteria
by: Jang, Joonwon, et al.
Published: (2024)
by: Jang, Joonwon, et al.
Published: (2024)
STEPER: Step-wise Knowledge Distillation for Enhancing Reasoning Ability in Multi-Step Retrieval-Augmented Language Models
by: Lee, Kyumin, et al.
Published: (2025)
by: Lee, Kyumin, et al.
Published: (2025)
Uncertainty Quantification and Decomposition for LLM-based Recommendation
by: Kweon, Wonbin, et al.
Published: (2025)
by: Kweon, Wonbin, et al.
Published: (2025)
Exploring Language Model's Code Generation Ability with Auxiliary Functions
by: Lee, Seonghyeon, et al.
Published: (2024)
by: Lee, Seonghyeon, et al.
Published: (2024)
How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code
by: Lee, Seonghyeon, et al.
Published: (2025)
by: Lee, Seonghyeon, et al.
Published: (2025)
Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation
by: Lee, Seonghyeon, et al.
Published: (2024)
by: Lee, Seonghyeon, et al.
Published: (2024)
Topic Coverage-based Demonstration Retrieval for In-Context Learning
by: Kweon, Wonbin, et al.
Published: (2025)
by: Kweon, Wonbin, et al.
Published: (2025)
Top-Personalized-K Recommendation
by: Kweon, Wonbin, et al.
Published: (2024)
by: Kweon, Wonbin, et al.
Published: (2024)
BPL: Bias-adaptive Preference Distillation Learning for Recommender System
by: Kang, SeongKu, et al.
Published: (2025)
by: Kang, SeongKu, et al.
Published: (2025)
Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders
by: Lee, Jaehyun, et al.
Published: (2026)
by: Lee, Jaehyun, et al.
Published: (2026)
Shortcut Learning in In-Context Learning: A Survey
by: Song, Rui, et al.
Published: (2024)
by: Song, Rui, et al.
Published: (2024)
Are Vision-Language Models Safe in the Wild? A Meme-Based Benchmark Study
by: Lee, DongGeon, et al.
Published: (2025)
by: Lee, DongGeon, et al.
Published: (2025)
From What to Respond to When to Respond: Timely Response Generation for Open-domain Dialogue Agents
by: Jang, Seongbo, et al.
Published: (2025)
by: Jang, Seongbo, et al.
Published: (2025)
Condition-Gated Reasoning for Context-Dependent Biomedical Question Answering
by: Parekh, Jash Rajesh, et al.
Published: (2026)
by: Parekh, Jash Rajesh, et al.
Published: (2026)
Harmonic Dataset Distillation for Time Series Forecasting
by: Hong, Seungha, et al.
Published: (2026)
by: Hong, Seungha, et al.
Published: (2026)
REFIND at SemEval-2025 Task 3: Retrieval-Augmented Factuality Hallucination Detection in Large Language Models
by: Lee, DongGeon, et al.
Published: (2025)
by: Lee, DongGeon, et al.
Published: (2025)
Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning
by: Kim, Jeonghoon, et al.
Published: (2024)
by: Kim, Jeonghoon, et al.
Published: (2024)
In-Context Learning with Iterative Demonstration Selection
by: Qin, Chengwei, et al.
Published: (2023)
by: Qin, Chengwei, et al.
Published: (2023)
Dynamic Demonstrations Controller for In-Context Learning
by: Zhao, Fei, et al.
Published: (2023)
by: Zhao, Fei, et al.
Published: (2023)
Comparable Demonstrations are Important in In-Context Learning: A Novel Perspective on Demonstration Selection
by: Fan, Caoyun, et al.
Published: (2023)
by: Fan, Caoyun, et al.
Published: (2023)
Demonstration Selection for In-Context Learning via Reinforcement Learning
by: Wang, Xubin, et al.
Published: (2024)
by: Wang, Xubin, et al.
Published: (2024)
Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random
by: Kweon, Wonbin, et al.
Published: (2024)
by: Kweon, Wonbin, et al.
Published: (2024)
The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis
by: Zhang, Miaoran, et al.
Published: (2024)
by: Zhang, Miaoran, et al.
Published: (2024)
Towards Understanding In-Context Learning with Contrastive Demonstrations and Saliency Maps
by: Liu, Fuxiao, et al.
Published: (2023)
by: Liu, Fuxiao, et al.
Published: (2023)
Predicting Sentence Acceptability Judgments in Multimodal Contexts
by: Jang, Hyewon, et al.
Published: (2026)
by: Jang, Hyewon, et al.
Published: (2026)
On the Shortcut Learning in Multilingual Neural Machine Translation
by: Wang, Wenxuan, et al.
Published: (2024)
by: Wang, Wenxuan, et al.
Published: (2024)
Chimera: Diagnosing Shortcut Learning in Visual-Language Understanding
by: Chi, Ziheng, et al.
Published: (2025)
by: Chi, Ziheng, et al.
Published: (2025)
COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs
by: Choi, Dasol, et al.
Published: (2026)
by: Choi, Dasol, 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)
Demonstration Notebook: Finding the Most Suited In-Context Learning Example from Interactions
by: Tang, Yiming, et al.
Published: (2024)
by: Tang, Yiming, et al.
Published: (2024)
Modulated Intervention Preference Optimization (MIPO): Keep the Easy, Refine the Difficult
by: Jang, Cheolhun
Published: (2024)
by: Jang, Cheolhun
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)
Demonstrations Are All You Need: Advancing Offensive Content Paraphrasing using In-Context Learning
by: Som, Anirudh, et al.
Published: (2023)
by: Som, Anirudh, et al.
Published: (2023)
Optimizing In-Context Demonstrations for LLM-based Automated Grading
by: Chu, Yucheng, et al.
Published: (2026)
by: Chu, Yucheng, et al.
Published: (2026)
Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning
by: Bae, Sanghwan, et al.
Published: (2025)
by: Bae, Sanghwan, et al.
Published: (2025)
Toward Robust In-Context Learning: Leveraging Out-of-distribution Proxies for Target Inaccessible Demonstration Retrieval
by: Xu, Hao, et al.
Published: (2026)
by: Xu, Hao, et al.
Published: (2026)
KoDialogBench: Evaluating Conversational Understanding of Language Models with Korean Dialogue Benchmark
by: Jang, Seongbo, et al.
Published: (2024)
by: Jang, Seongbo, et al.
Published: (2024)
Comparative Analysis of Demonstration Selection Algorithms for LLM In-Context Learning
by: Shu, Dong, et al.
Published: (2024)
by: Shu, Dong, et al.
Published: (2024)
Retrieval-Augmented Fine-Tuning With Preference Optimization For Visual Program Generation
by: Kang, Deokhyung, et al.
Published: (2025)
by: Kang, Deokhyung, et al.
Published: (2025)
Revealing and Mitigating the Local Pattern Shortcuts of Mamba
by: You, Wangjie, et al.
Published: (2024)
by: You, Wangjie, et al.
Published: (2024)
Similar Items
-
Verbosity-Aware Rationale Reduction: Effective Reduction of Redundant Rationale via Principled Criteria
by: Jang, Joonwon, et al.
Published: (2024) -
STEPER: Step-wise Knowledge Distillation for Enhancing Reasoning Ability in Multi-Step Retrieval-Augmented Language Models
by: Lee, Kyumin, et al.
Published: (2025) -
Uncertainty Quantification and Decomposition for LLM-based Recommendation
by: Kweon, Wonbin, et al.
Published: (2025) -
Exploring Language Model's Code Generation Ability with Auxiliary Functions
by: Lee, Seonghyeon, et al.
Published: (2024) -
How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code
by: Lee, Seonghyeon, et al.
Published: (2025)