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In-Context Learning

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TitleStatusHype
A Latent Space Theory for Emergent Abilities in Large Language Models—0
Towards Robust Prompts on Vision-Language Models—0
Extractive Summarization via ChatGPT for Faithful Summary Generation—0
Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions—0
Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation—0
Resources and Few-shot Learners for In-context Learning in Slavic LanguagesCode0
Subject-driven Text-to-Image Generation via Apprenticeship Learning—0
Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context LearningCode0
TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs—0
When Brain-inspired AI Meets AGI—0
GPT is becoming a Turing machine: Here are some ways to program it—0
Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System—0
Fairness-guided Few-shot Prompting for Large Language Models—0
The Scope of In-Context Learning for the Extraction of Medical Temporal Constraints—0
The Learnability of In-Context Learning—0
A Theory of Emergent In-Context Learning as Implicit Structure Induction—0
How Many Demonstrations Do You Need for In-context Learning?—0
MenuCraft: Interactive Menu System Design with Large Language ModelsCode0
Larger language models do in-context learning differently—0
Finding Support Examples for In-Context Learning—0
How Does In-Context Learning Help Prompt Tuning?—0
Few-shot Multimodal Multitask Multilingual Learning—0
Conversation Style Transfer using Few-Shot Learning—0
Do We Still Need Clinical Language Models?Code0
ScatterShot: Interactive In-context Example Curation for Text TransformationCode0
Distinguishability Calibration to In-Context LearningCode0
Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking—0
Context Understanding in Computer Vision: A Survey—0
CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models—0
Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learning—0
Towards Few-Shot Identification of Morality Frames using In-Context Learning—0
Using In-Context Learning to Improve Dialogue Safety—0
Collaborating with language models for embodied reasoning—0
Looped Transformers as Programmable ComputersCode0
Human-Timescale Adaptation in an Open-Ended Task Space—0
Transformers as Algorithms: Generalization and Stability in In-context LearningCode0
SegGPT: Towards Segmenting Everything in Context—0
Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-shot In-Context Learners—0
In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models—0
Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers—0
HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation—0
Ontologically Faithful Generation of Non-Player Character Dialogues—0
Dialog2API: Task-Oriented Dialogue with API Description and Example Programs—0
Language model acceptability judgements are not always robust to context—0
Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated?—0
Foundation models in brief: A historical, socio-technical focus—0
Evaluating Step-by-Step Reasoning through Symbolic VerificationCode0
Structured Prompting: Scaling In-Context Learning to 1,000 Examples—0
General-Purpose In-Context Learning by Meta-Learning TransformersCode0
In-context Examples Selection for Machine TranslationCode0
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