Papers In-Context Learning
“In-Context Learning” 태그가 달린 논문 2,297편 · 필터 해제
When and How Unlabeled Data Provably Improve In-Context Learning
Recent research shows that in-context learning (ICL) can be effective even when demonstrations have missing or incorrect labels. To shed light on this capability, we examine a canonical setting where the demonstrations a…
In-Context LearningMissing LabelsConquering the Retina: Bringing Visual in-Context Learning to OCT
Recent advancements in medical image analysis have led to the development of highly specialized models tailored to specific clinical tasks. These models have demonstrated exceptional performance and remain a crucial rese…
In-Context LearningMedical Image AnalysisVela: Scalable Embeddings with Voice Large Language Models for Multimodal Retrieval
Multimodal large language models (MLLMs) have seen substantial progress in recent years. However, their ability to represent multimodal information in the acoustic domain remains underexplored. In this work, we introduce…
In-Context LearningRetrievalRevisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot
In-Context Learning (ICL) is an essential emergent ability of Large Language Models (LLMs), and recent studies introduce Chain-of-Thought (CoT) to exemplars of ICL to enhance the reasoning capability, especially in mathe…
In-Context LearningMathematical ReasoningAssessing the Limits of In-Context Learning beyond Functions using Partially Ordered Relation
Generating rational and generally accurate responses to tasks, often accompanied by example demonstrations, highlights Large Language Model's (LLM's) remarkable In-Context Learning (ICL) capabilities without requiring up…
In-Context LearningRelationLeveraging In-Context Learning for Language Model Agents
In-context learning (ICL) with dynamically selected demonstrations combines the flexibility of prompting large language models (LLMs) with the ability to leverage training data to improve performance. While ICL has been …
In-Context LearningLanguage ModelingLanguage ModellingSequential Decision MakingSurprise Calibration for Better In-Context Learning
In-context learning (ICL) has emerged as a powerful paradigm for task adaptation in large language models (LLMs), where models infer underlying task structures from a few demonstrations. However, ICL remains susceptible …
Bayesian InferenceIn-Context LearningSequential Bayesian InferenceEvaluating Cell Type Inference in Vision Language Models Under Varying Visual Context
Vision-Language Models (VLMs) have rapidly advanced alongside Large Language Models (LLMs). This study evaluates the capabilities of prominent generative VLMs, such as GPT-4.1 and Gemini 2.5 Pro, accessed via APIs, for h…
image-classificationImage ClassificationIn-Context LearningPRO-V: An Efficient Program Generation Multi-Agent System for Automatic RTL Verification
LLM-assisted hardware verification is gaining substantial attention due to its potential to significantly reduce the cost and effort of crafting effective testbenches. It also serves as a critical enabler for LLM-aided e…
Code GenerationIn-Context LearningLarge Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning
Family caregivers often face substantial mental health challenges due to their multifaceted roles and limited resources. This study explored the potential of a large language model (LLM)-powered conversational agent to d…
In-Context LearningLanguage ModelingLanguage ModellingLarge Language Model+2Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AI
Artificial Intelligence (AI) is rapidly embedded in critical decision-making systems, however their foundational ``black-box'' models require eXplainable AI (XAI) solutions to enhance transparency, which are mostly orien…
BenchmarkingIn-Context LearningGraph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in representing and understanding diverse modalities. However, they typically focus on modality alignment in a pairwise manner while over…
Graph LearningIn-Context LearningRecommendation SystemsConTextTab: A Semantics-Aware Tabular In-Context Learner
Tabular in-context learning (ICL) has recently achieved state-of-the-art (SOTA) performance on several tabular prediction tasks. Previously restricted to classification problems on small tables, recent advances such as T…
In-Context LearningWorld KnowledgeFoundation Models for Causal Inference via Prior-Data Fitted Networks
Prior-data fitted networks (PFNs) have recently been proposed as a promising way to train tabular foundation models. PFNs are transformers that are pre-trained on synthetic data generated from a prespecified prior distri…
Bayesian InferenceCausal InferenceIn-Context LearningUnderstanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
While in-context learning (ICL) has achieved remarkable success in natural language and vision domains, its theoretical understanding--particularly in the context of structured geometric data--remains unexplored. In this…
In-Context LearningregressionTest-Time Adaptation for Generalizable Task Progress Estimation
We propose a test-time adaptation method that enables a progress estimation model to adapt online to the visual and temporal context of test trajectories by optimizing a learned self-supervised objective. To this end, we…
In-Context LearningMeta-LearningTest-time AdaptationProvoking Multi-modal Few-Shot LVLM via Exploration-Exploitation In-Context Learning
In-context learning (ICL), a predominant trend in instruction learning, aims at enhancing the performance of large language models by providing clear task guidance and examples, improving their capability in task underst…
In-Context LearningQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)Towards Multi-modal Graph Large Language Model
Multi-modal graphs, which integrate diverse multi-modal features and relations, are ubiquitous in real-world applications. However, existing multi-modal graph learning methods are typically trained from scratch for speci…
Graph LearningIn-Context LearningLanguage ModelingLanguage Modelling+2Factors affecting the in-context learning abilities of LLMs for dialogue state tracking
This study explores the application of in-context learning (ICL) to the dialogue state tracking (DST) problem and investigates the factors that influence its effectiveness. We use a sentence embedding based k-nearest nei…
Dialogue State TrackingIn-Context LearningSentenceSentence Embedding+1Sample Efficient Demonstration Selection for In-Context Learning
The in-context learning paradigm with LLMs has been instrumental in advancing a wide range of natural language processing tasks. The selection of few-shot examples (exemplars / demonstration samples) is essential for con…
In-Context Learning