Papers In-Context Learning
“In-Context Learning” 태그가 달린 논문 2,297편 · 필터 해제
Enhancing Cross-task Transfer of Large Language Models via Activation Steering
Large language models (LLMs) have shown impressive abilities in leveraging pretrained knowledge through prompting, but they often struggle with unseen tasks, particularly in data-scarce scenarios. While cross-task in-con…
Cross-Lingual TransferIn-Context LearningAssay2Mol: large language model-based drug design using BioAssay context
Scientific databases aggregate vast amounts of quantitative data alongside descriptive text. In biochemistry, molecule screening assays evaluate the functional responses of candidate molecules against disease targets. Un…
DescriptiveDrug DesignDrug DiscoveryIn-Context Learning+3Journalism-Guided Agentic In-Context Learning for News Stance Detection
As online news consumption grows, personalized recommendation systems have become integral to digital journalism. However, these systems risk reinforcing filter bubbles and political polarization by failing to incorporat…
ArticlesIn-Context LearningPositionRecommendation Systems+1DocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations
Large, high-quality annotated corpora remain scarce in document-level entity and relation extraction in zero-shot or few-shot settings. In this paper, we present a fully automatic, LLM-based pipeline for synthetic data g…
In-Context LearningJoint Entity and Relation ExtractionRelationRelation Extraction+1Meta-Learning Transformers to Improve In-Context Generalization
In-context learning enables transformer models to generalize to new tasks based solely on input prompts, without any need for weight updates. However, existing training paradigms typically rely on large, unstructured dat…
In-Context LearningMeta-LearningICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks
In-context learning (ICL) has demonstrated remarkable success in large language models (LLMs) due to its adaptability and parameter-free nature. However, it also introduces a critical vulnerability to backdoor attacks, w…
In-Context LearningChain-of-Thought Enhanced Shallow Transformers for Wireless Symbol Detection
Transformers have shown potential in solving wireless communication problems, particularly via in-context learning (ICL), where models adapt to new tasks through prompts without requiring model updates. However, prior IC…
Computational EfficiencyIn-Context LearningSMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning
Multimodal in-context learning (ICL) remains underexplored despite significant potential for domains such as medicine. Clinicians routinely encounter diverse, specialized tasks requiring adaptation from limited examples,…
In-Context LearningMedical Visual Question AnsweringQuestion AnsweringVisual Question Answering+1Early Stopping Tabular In-Context Learning
Tabular foundation models have shown strong performance across various tabular learning tasks via in-context learning, offering robust generalization without any downstream finetuning. However, their inference-time costs…
DecoderIn-Context LearningCase-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios
Driving in safety-critical scenarios requires quick, context-aware decision-making grounded in both situational understanding and experiential reasoning. Large Language Models (LLMs), with their powerful general-purpose …
Autonomous DrivingDecision MakingDomain AdaptationIn-Context Learning+5A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs
Spatio-temporal data mining plays a pivotal role in informed decision making across diverse domains. However, existing models are often restricted to narrow tasks, lacking the capacity for multi-task inference and comple…
In-Context LearningNatural Language QueriesHow to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models?
Large language models (LLMs) have enabled a wide variety of real-world applications in various domains. However, creating a high-performing application with high accuracy remains challenging, particularly for subjective …
Emotion RecognitionIn-Context LearningRetrievalSV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models
Ensuring the security of complex system-on-chips (SoCs) designs is a critical imperative, yet traditional verification techniques struggle to keep pace due to significant challenges in automation, scalability, comprehens…
Code GenerationIn-Context LearningNatural Language UnderstandingQuestion Answering+3Automatic Demonstration Selection for LLM-based Tabular Data Classification
A fundamental question in applying In-Context Learning (ICL) for tabular data classification is how to determine the ideal number of demonstrations in the prompt. This work addresses this challenge by presenting an algor…
In-Context LearningLanguage ModelingLanguage ModellingLarge Language ModelUniversal pre-training by iterated random computation
We investigate the use of randomly generated data for the sake of pre-training a model. We justify this approach theoretically from the perspective of algorithmic complexity, building on recent research that shows that s…
In-Context LearningFrom Memories to Maps: Mechanisms of In-Context Reinforcement Learning in Transformers
Humans and animals show remarkable learning efficiency, adapting to new environments with minimal experience. This capability is not well captured by standard reinforcement learning algorithms that rely on incremental va…
In-Context LearningIn-Context Reinforcement Learningreinforcement-learningReinforcement LearningAgenticControl: An Automated Control Design Framework Using Large Language Models
Traditional control system design, reliant on expert knowledge and precise models, struggles with complex, nonlinear, or uncertain dynamics. This paper introduces AgenticControl, a novel multi-agent framework that automa…
In-Context LearningLarge Language ModelModel Predictive ControlEvolving Prompts In-Context: An Open-ended, Self-replicating Perspective
We propose a novel prompt design paradigm that challenges conventional wisdom in large language model (LLM) prompting. While conventional wisdom prioritizes well-crafted instructions and demonstrations for in-context lea…
In-Context LearningLarge Language ModelMathQuestion AnsweringIn-Context Learning Strategies Emerge Rationally
Recent work analyzing in-context learning (ICL) has identified a broad set of strategies that describe model behavior in different experimental conditions. We aim to unify these findings by asking why a model learns thes…
In-Context LearningMemorizationIn-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory
In recent years, deep learning has facilitated the creation of wireless receivers capable of functioning effectively in conditions that challenge traditional model-based designs. Leveraging programmable hardware architec…
In-Context LearningMeta-LearningState Space Models